R Packages

All packages loaded during this work.

library(readxl)
library(tidyverse)
library(ec50estimator)
library(cowplot)
library(ggthemes)
library(drc)
library(ggridges)
library(epiR)
library(car)
library(gsheet)
library(patchwork)
library(rnaturalearth)
library(ggmap)
library(ggrepel)
library(lme4)
library(lmerTest)
library(ggsn)

Isolates

All isolates used in this study with their location were loaded to plotting the map below.

data_load = gsheet2tbl("https://docs.google.com/spreadsheets/d/1YTmns-Mjyz6HWluKN5ksHCpJfD_jshavGVI52oLfszU/edit#gid=1862629108") 

data_load
## # A tibble: 230 × 10
##    Isolate `Cód. Origem isolado` Trial `LMX Species ID` Hospedeiro safra município
##    <chr>   <chr>                 <chr> <chr>            <chr>      <dbl> <chr>    
##  1 FGT1    07Tr003               DD    F.graminearum    trigo       2007 Cruz Alta
##  2 FGT2    07Tr004               DD    F.graminearum    trigo       2007 Cruz Alta
##  3 FGT3    07Tr039               DD    F.graminearum    trigo       2007 Cruz Alta
##  4 FGT4    07Tr023               DD    F.graminearum    trigo       2007 Cruz Alta
##  5 FGT5    07Tr037               EC    F.graminearum    trigo       2007 Cruz Alta
##  6 FGT6    07Tr012               EC    F.graminearum    trigo       2007 Cruz Alta
##  7 FGT7    07Tr013               EC    F.graminearum    trigo       2007 Cruz Alta
##  8 5       07Tr070               DD    F.graminearum    trigo       2007 Cruz Alta
##  9 FGT8    07Tr009               DD    F.graminearum    trigo       2007 Cruz Alta
## 10 FGT9    07Tr132               DD    F.graminearum    trigo       2007 Ernestina
## # … with 220 more rows, and 3 more variables: estado <chr>, lat <dbl>,
## #   long <dbl>

By year and state

map_all  = data_load %>% 
  group_by(safra, estado) %>%
  summarise(n = length(Isolate)) 
## `summarise()` has grouped output by 'safra'. You can override using the `.groups` argument.
map_all
## # A tibble: 12 × 3
## # Groups:   safra [11]
##    safra estado     n
##    <dbl> <chr>  <int>
##  1  2007 RS        23
##  2  2008 RS        29
##  3  2009 RS        27
##  4  2010 RS        28
##  5  2011 PR         6
##  6  2011 RS        23
##  7  2012 PR         6
##  8  2013 PR         6
##  9  2014 PR         7
## 10  2018 PR        62
## 11  2019 PR         6
## 12  2020 PR         7

By year in RS

p1 <- map_all %>% 
  filter(estado=="RS") %>% 
ggplot(aes(factor(safra), y= n))+
  ylim(0,60)+
  geom_col( position = "dodge",width = 0.5, color="black",fill="black", alpha=0.6)+
  scale_fill_colorblind()+
  theme_minimal()+
  labs(x = "Year", y = "Count of Isolates", fill="")

ggsave("img/n_year_rs.png",width = 5, height = 3, dpi = 300)

By year in PR

p2 <- map_all %>% 
  filter(estado=="PR") %>% 
ggplot(aes(factor(safra), y= n))+
  geom_col( position = "dodge",width = 0.5, color="black",fill="orange", alpha=0.6)+
  scale_fill_colorblind()+
  theme_minimal()+
  labs(x = "Year", y = "Count of Isolates", fill="")

ggsave("img/n_year_pr.png",width = 5, height = 3, dpi = 300)
#install.packages("devtools") 
#devtools::install_github("ropensci/rnaturalearthhires")
library("rnaturalearth")

BRA <- ne_states(country = "Brazil",
  returnclass = "sf")

By municipality

map_all_1  = data_load %>% 
  group_by(estado, município) %>%
  summarise(n = length(Isolate),
            lat=mean(lat), 
            long= mean(long)) 
## `summarise()` has grouped output by 'estado'. You can override using the `.groups` argument.
map_all_1
## # A tibble: 36 × 5
## # Groups:   estado [2]
##    estado município             n   lat  long
##    <chr>  <chr>             <int> <dbl> <dbl>
##  1 PR     Campina do Simão      1 -25.1 -51.8
##  2 PR     Candói                3 -25.5 -52.0
##  3 PR     Foz do Jordão         2 -25.7 -52.1
##  4 PR     Goioxim               1 -25.2 -52.0
##  5 PR     Goixin                1 -25.2 -52.0
##  6 PR     Guarapuava           82 -25.4 -51.5
##  7 PR     Pinhão                4 -25.7 -51.7
##  8 PR     Pitanga               3 -24.8 -51.8
##  9 PR     Reserva do Iguaçu     2 -25.8 -51.9
## 10 PR     Turvo                 1 -25.0 -51.5
## # … with 26 more rows

Map of isolates

p3 <- ggplot() +
  geom_sf(data = BRA, fill = "white",color = "black")+
geom_point(data = map_all_1, aes(as.numeric(long),as.numeric(lat), color= estado, fill= estado, size=n), shape = 21, width = 0.9)+
geom_text_repel(data = map_all_1, aes(as.numeric(long),as.numeric(lat), label=(n)), size = 4, box.padding = 0.2, min.segment.length =1, seed = F, max.overlaps = Inf)+
  xlim(-58,-48)+
  ylim(-34,-23)+
  #scale_size_continuous(breaks = seq(1,10, by = 1), range = c(1, 6))+
  #facet_wrap(~safra,nrow = 2)+
  theme_map()+
  scale_color_manual(values = c("orange","black"))+
  scale_fill_manual(values = c("orange","black"))+
  guides(color=F, size=F)+
  
 # theme(legend.position = "none")+
  labs(fill="", size="", x="", y="", color="" )+
   annotate("text", x=-53, y=-24, label="PR", size=4)+
  annotate("text", x=-54, y=-30, label="RS", size=4)
## Warning: Ignoring unknown parameters: width
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
 p3

  ggsave("img/map_new_5.png", dpi= 600, height = 8, width = 8 )
library(patchwork)
(p3 | (p1/p2)) +
  plot_annotation(tag_levels = "A")+
  plot_layout(widths = c(2, 1))

ggsave("img/figure1.png", width =8, height =5)

In this study, we phenotypically characterize Fusarium graminearum isolates to azoxystrobin and pyraclostrobin.

EC50 - Pyra

Data

This database contains information about all germination tests conducted, divided by: experiment and geographic collection.

germination_assay<- gsheet2tbl("https://docs.google.com/spreadsheets/d/13uMSrA-gLXXhkgTKxrE0MjZX1ewLZGxnTUIpWCD-n64/edit#gid=1516403512") %>% 
  group_by(Isolate, Dose, Replicates, Trail) %>%
  dplyr::mutate(GC = as.numeric(GC)) %>%
  dplyr::mutate(Dose = as.numeric(Dose))
  
germination_assay
## # A tibble: 1,200 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [1,200]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT05       07Tr037  2007     1 RS          0             1    46
##  2 FGT05       07Tr037  2007     1 RS          0             2    44
##  3 FGT05       07Tr037  2007     1 RS          0.05          1    18
##  4 FGT05       07Tr037  2007     1 RS          0.05          2    24
##  5 FGT05       07Tr037  2007     1 RS          0.5           1     9
##  6 FGT05       07Tr037  2007     1 RS          0.5           2    11
##  7 FGT05       07Tr037  2007     1 RS          1             1     0
##  8 FGT05       07Tr037  2007     1 RS          1             2     0
##  9 FGT05       07Tr037  2007     1 RS          5             1     0
## 10 FGT05       07Tr037  2007     1 RS          5             2     0
## # … with 1,190 more rows

Visualization

germination_assay %>% 
  ggplot(aes(as.factor(Dose), GC, color=as.factor(Trail)))+
    stat_summary(fun.data = "mean_cl_boot",
               geom = "errorbar",
               width = 0,
               size =.5,
               position = position_dodge(width=.5))+
  stat_summary(fun = "mean",
               size = 1.4,
               geom = "point",
               position = position_dodge(width=.5))+
  facet_wrap(~Isolate)+
  scale_color_colorblind()+
  theme_minimal_hgrid(font_size = 6)+
   labs(x = expression(Dose~~μg/ml), 
        y = "Number of germinated conidia/dose", color="Trial")

Models

Ten isolates were chosen to select the best model that fits all the data set.

Isolates: “09Tr006”

g1<-germination_assay %>% 
  filter(Isolate=="07Tr037")
g1
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT05       07Tr037  2007     1 RS          0             1    46
##  2 FGT05       07Tr037  2007     1 RS          0             2    44
##  3 FGT05       07Tr037  2007     1 RS          0.05          1    18
##  4 FGT05       07Tr037  2007     1 RS          0.05          2    24
##  5 FGT05       07Tr037  2007     1 RS          0.5           1     9
##  6 FGT05       07Tr037  2007     1 RS          0.5           2    11
##  7 FGT05       07Tr037  2007     1 RS          1             1     0
##  8 FGT05       07Tr037  2007     1 RS          1             2     0
##  9 FGT05       07Tr037  2007     1 RS          5             1     0
## 10 FGT05       07Tr037  2007     1 RS          5             2     0
## # … with 14 more rows
model_1=drm(GC~Dose, data=g1, fct = W1.3())
plot(model_1)

summary(model_1)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.414392   0.046065  8.9957 1.195e-08 ***
## d:(Intercept) 46.719086   1.259436 37.0952 < 2.2e-16 ***
## e:(Intercept)  0.069258   0.011948  5.7964 9.394e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  2.516979 (21 degrees of freedom)
ED(model_1,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.028599   0.006745
mselect(model_1,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit    Res var
## W1.3    -54.60557 117.2111 2.279955e-01   6.335181
## W1.3    -54.60557 117.2111 2.279955e-01   6.335181
## W1.4    -54.50537 119.0107 1.308674e-01   6.596627
## LL.3    -55.81557 119.6311 1.026525e-01   7.007297
## LL2.3   -55.81557 119.6311 1.026525e-01   7.007297
## W2.3    -56.09034 120.1807 8.526332e-02   7.169599
## LL.4    -55.21835 120.4367 7.666541e-02   7.000443
## BC.5    -54.26618 120.5324 5.748197e-02   6.806783
## W2.4    -55.38144 120.7629 6.783868e-02   7.096234
## LL2.5   -54.71011 121.4202 3.909036e-02   7.063310
## LL.5    -54.75607 121.5121 3.757703e-02   7.090414
## LL2.4   -55.81459 121.6292 4.902204e-02   7.357058
## AR.3    -61.11189 130.2238 2.486918e-03  10.895082
## W1.2   -106.35864 218.7173 1.670998e-17 451.400295
## LL.2   -106.35888 218.7178 1.670703e-17 451.409140
## LL2.2  -106.37095 218.7419 1.655656e-17 451.863636
## AR.2   -106.39170 218.7834 1.630123e-17 452.645455
## W2.2   -106.44860 218.8972 1.562089e-17 454.796940
## LL.3u  -106.35878 220.7176 5.916175e-18 472.900950
## LL2.3u -106.37095 220.7419 5.862432e-18 473.380952
## W1.3u          NA       NA           NA         NA
## MM.2           NA       NA           NA         NA
## MM.3           NA       NA           NA         NA

“UEM-2665”

g2<-germination_assay %>% 
  filter(Isolate=="08Tr005")
g2
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT35       08Tr005  2008     1 RS          0             1    50
##  2 FGT35       08Tr005  2008     1 RS          0             2    49
##  3 FGT35       08Tr005  2008     1 RS          0.05          1    40
##  4 FGT35       08Tr005  2008     1 RS          0.05          2    43
##  5 FGT35       08Tr005  2008     1 RS          0.5           1    11
##  6 FGT35       08Tr005  2008     1 RS          0.5           2    16
##  7 FGT35       08Tr005  2008     1 RS          1             1     4
##  8 FGT35       08Tr005  2008     1 RS          1             2     2
##  9 FGT35       08Tr005  2008     1 RS          5             1     0
## 10 FGT35       08Tr005  2008     1 RS          5             2     0
## # … with 14 more rows
model_2=drm((GC)~Dose, data=g2, fct = W1.3())
plot(model_2)

summary(model_2)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  1.086482   0.085787  12.665 2.676e-11 ***
## d:(Intercept) 49.776520   0.853592  58.314 < 2.2e-16 ***
## e:(Intercept)  0.372232   0.020087  18.531 1.712e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  1.766687 (21 degrees of freedom)
ED(model_2,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.265649   0.019286
mselect(model_2,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit    Res var
## W1.3    -46.11069 100.2214 9.996229e-01   3.121183
## W1.3    -46.11069 100.2214 9.996229e-01   3.121183
## W1.4    -46.10999 102.2200 9.943820e-01   3.277051
## LL.5    -46.10484 104.2097 9.532342e-01   3.448046
## LL2.5   -46.11198 104.2240 9.062661e-01   3.450100
## BC.5    -46.67499 105.3500 3.607314e-01   3.615826
## BC.4    -48.15981 106.3196 2.137401e-01   3.887482
## LL2.3   -49.47962 106.9592 1.577266e-01   4.132804
## LL.3    -49.47963 106.9593 1.577258e-01   4.132807
## LL.4    -48.94877 107.8975 1.182784e-01   4.151660
## LL2.4   -49.47805 108.9561 7.952525e-02   4.338878
## W2.4    -51.89926 113.7985 1.293801e-02   5.308891
## W2.3    -53.41676 114.8335 1.042904e-02   5.737659
## W2.2   -113.28422 232.5684 1.306337e-21 803.909092
## LL.2   -113.28694 232.5739 1.303682e-21 804.090909
## W1.2   -113.28694 232.5739 1.303682e-21 804.090909
## LL2.2  -113.28694 232.5739 1.303682e-21 804.090909
## LL.3u  -113.28694 234.5739 4.601771e-22 842.380952
## W1.3u  -113.28694 234.5739 4.601771e-22 842.380952
## LL2.3u -113.28694 234.5739 4.601771e-22 842.380952
## AR.2           NA       NA           NA         NA
## AR.3           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA
## MM.3           NA       NA           NA         NA

“09Tr006”

g3<-germination_assay %>% 
  filter(Isolate=="09Tr006")
g3
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT63       09Tr006  2009     1 RS          0             1    48
##  2 FGT63       09Tr006  2009     1 RS          0             2    50
##  3 FGT63       09Tr006  2009     1 RS          0.05          1    36
##  4 FGT63       09Tr006  2009     1 RS          0.05          2    40
##  5 FGT63       09Tr006  2009     1 RS          0.5           1    16
##  6 FGT63       09Tr006  2009     1 RS          0.5           2    15
##  7 FGT63       09Tr006  2009     1 RS          1             1     7
##  8 FGT63       09Tr006  2009     1 RS          1             2     8
##  9 FGT63       09Tr006  2009     1 RS          5             1     0
## 10 FGT63       09Tr006  2009     1 RS          5             2     0
## # … with 14 more rows
model_3=drm((GC)~Dose, data=g3, fct = LL.4())
plot(model_3)

summary(model_3)
## 
## Model fitted: Log-logistic (ED50 as parameter) (4 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  1.013245   0.071826 14.1070 7.422e-12 ***
## c:(Intercept) -1.524378   0.914537 -1.6668    0.1111    
## d:(Intercept) 49.503805   0.910898 54.3461 < 2.2e-16 ***
## e:(Intercept)  0.235947   0.021766 10.8402 8.012e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  1.83369 (20 degrees of freedom)
ED(model_3,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.235947   0.021766
mselect(model_3,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit    Res var
## BC.4    -46.41364 102.8273 6.205798e-01   3.361032
## LL.4    -46.41860 102.8372 6.182784e-01   3.362420
## LL.4    -46.41860 102.8372 6.182784e-01   3.362420
## W2.4    -46.86065 103.7213 4.438115e-01   3.488593
## W1.3    -47.98720 103.9744 3.335461e-01   3.649490
## LL.3    -48.22808 104.4562 2.869326e-01   3.723489
## LL2.3   -48.22808 104.4562 2.869326e-01   3.723489
## BC.5    -46.41399 104.8280 3.352004e-01   3.538032
## LL.5    -46.72594 105.4519 2.394279e-01   3.631212
## LL2.5   -46.92579 105.8516 1.955054e-01   3.692191
## W1.4    -47.97277 105.9455 1.927335e-01   3.827361
## LL2.4   -48.22579 106.4516 1.594201e-01   3.908918
## W2.3    -52.06587 112.1317 2.151831e-02   5.126776
## AR.3    -54.92772 117.8554 2.806161e-03   6.507560
## AR.2   -110.80580 227.6116 6.564358e-21 653.897947
## LL.2   -112.62958 231.2592 1.672613e-21 761.227797
## W1.2   -112.63530 231.2706 1.665452e-21 761.590909
## W2.2   -112.63530 231.2706 1.665452e-21 761.590909
## LL2.2  -112.63530 231.2706 1.665452e-21 761.590909
## LL.3u  -112.62960 233.2592 5.904242e-22 797.477775
## W1.3u  -112.63530 233.2706 5.879029e-22 797.857143
## LL2.3u -112.63530 233.2706 5.879029e-22 797.857143
## MM.2           NA       NA           NA         NA
## MM.3           NA       NA           NA         NA

“10Tr042”

g4<-germination_assay %>% 
  filter(Isolate=="10Tr042")
g4
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT103      10Tr042  2010     1 RS          0             1    47
##  2 FGT103      10Tr042  2010     1 RS          0             2    48
##  3 FGT103      10Tr042  2010     1 RS          0.05          1    29
##  4 FGT103      10Tr042  2010     1 RS          0.05          2    31
##  5 FGT103      10Tr042  2010     1 RS          0.5           1     3
##  6 FGT103      10Tr042  2010     1 RS          0.5           2     5
##  7 FGT103      10Tr042  2010     1 RS          1             1     3
##  8 FGT103      10Tr042  2010     1 RS          1             2     3
##  9 FGT103      10Tr042  2010     1 RS          5             1     0
## 10 FGT103      10Tr042  2010     1 RS          5             2     0
## # … with 14 more rows
model_4=drm((GC)~Dose, data=g4, fct = W1.3())
plot(model_4)

summary(model_4)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.70312    0.19039   3.693 0.0013502 ** 
## d:(Intercept) 47.67086    3.47890  13.703 6.073e-12 ***
## e:(Intercept)  0.42582    0.10810   3.939 0.0007516 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  6.988745 (21 degrees of freedom)
ED(model_4,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50  0.25284    0.08476
mselect(model_4,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit   Res var
## W1.3    -79.11537 166.2307 9.975693e-01  48.84255
## W1.3    -79.11537 166.2307 9.975693e-01  48.84255
## LL.3    -79.42028 166.8406 9.158392e-01  50.09949
## LL2.3   -79.42028 166.8406 9.158392e-01  50.09949
## AR.3    -79.79778 167.5956 7.781925e-01  51.70062
## W2.3    -79.90851 167.8170 7.372425e-01  52.17987
## W1.4    -79.11023 168.2205 9.825433e-01  51.26271
## LL.4    -79.26313 168.5263 8.760917e-01  51.92005
## BC.4    -79.26830 168.5366 8.727006e-01  51.94243
## W2.4    -79.41570 168.8314 7.813652e-01  52.58439
## LL2.4   -79.42009 168.8402 7.787952e-01  52.60365
## LL2.5   -79.11830 170.2366 8.301280e-01  53.99703
## LL.5    -79.12713 170.2543 8.082235e-01  54.03679
## BC.5    -79.26021 170.5204 6.149193e-01  54.63939
## AR.2   -110.54784 227.0957 5.285544e-10 639.99091
## W2.2   -112.30400 230.6080 1.429512e-10 740.85227
## LL.2   -112.30566 230.6113 1.427749e-10 740.95455
## W1.2   -112.30566 230.6113 1.427749e-10 740.95455
## LL2.2  -112.30566 230.6113 1.427749e-10 740.95455
## LL.3u  -112.30566 232.6113 5.179721e-11 776.23810
## W1.3u  -112.30566 232.6113 5.179721e-11 776.23810
## LL2.3u -112.30566 232.6113 5.179721e-11 776.23810
## MM.2           NA       NA           NA        NA
## MM.3           NA       NA           NA        NA

“11Tr005”

g5<-germination_assay %>% 
  filter(Isolate=="11Tr005")
g5
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT125      11Tr005  2011     1 RS          0             1    50
##  2 FGT125      11Tr005  2011     1 RS          0             2    50
##  3 FGT125      11Tr005  2011     1 RS          0.05          1    44
##  4 FGT125      11Tr005  2011     1 RS          0.05          2    47
##  5 FGT125      11Tr005  2011     1 RS          0.5           1    33
##  6 FGT125      11Tr005  2011     1 RS          0.5           2    28
##  7 FGT125      11Tr005  2011     1 RS          1             1    15
##  8 FGT125      11Tr005  2011     1 RS          1             2     8
##  9 FGT125      11Tr005  2011     1 RS          5             1     0
## 10 FGT125      11Tr005  2011     1 RS          5             2     2
## # … with 14 more rows
model_5=drm((GC)~Dose, data=g5, fct = W1.3())
plot(model_5)

summary(model_5)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  1.592285   0.185775   8.571  2.68e-08 ***
## d:(Intercept) 48.132350   0.916531  52.516 < 2.2e-16 ***
## e:(Intercept)  0.805420   0.030512  26.397 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  2.416801 (21 degrees of freedom)
ED(model_5,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.639818   0.028947
mselect(model_5,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit     Res var
## BC.5    -51.56867 115.1373 8.293316e-02    5.436454
## W1.3    -53.63083 115.2617 9.593524e-02    5.840929
## W1.3    -53.63083 115.2617 9.593524e-02    5.840929
## LL2.3   -54.12445 116.2489 6.861431e-02    6.086208
## LL.3    -54.12445 116.2489 6.861431e-02    6.086208
## W2.3    -54.15634 116.3127 6.713569e-02    6.102404
## BC.4    -53.42048 116.8410 5.319588e-02    6.026406
## W1.4    -53.45998 116.9200 5.164281e-02    6.046279
## LL2.4   -54.09923 118.1985 3.197370e-02    6.377102
## LL.4    -54.14657 118.2931 3.085846e-02    6.402308
## W2.4    -54.15179 118.3036 3.073785e-02    6.405094
## LL.5    -53.54598 119.0920 1.518668e-02    6.410277
## LL2.5   -54.12712 120.2542 9.406479e-03    6.728358
## AR.2   -112.34623 230.6925 3.391935e-20  743.463639
## W1.2   -115.64877 237.2975 2.852615e-21  979.000000
## W2.2   -115.64877 237.2975 2.852615e-21  979.000000
## LL2.2  -115.64877 237.2975 2.852615e-21  979.000000
## LL.2   -115.64879 237.2976 2.852570e-21  979.001726
## LL.3u  -115.64684 239.2937 1.008535e-21 1025.454204
## W1.3u  -115.64877 239.2975 1.007077e-21 1025.619048
## LL2.3u -115.64877 239.2975 1.007077e-21 1025.619048
## AR.3           NA       NA           NA          NA
## MM.2           NA       NA           NA          NA
## MM.3           NA       NA           NA          NA

“3072”

g6<-germination_assay %>% 
  filter(Isolate=="3072")
g6
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 189         3072     2012     1 PR          0             1    48
##  2 189         3072     2012     1 PR          0             2    49
##  3 189         3072     2012     1 PR          0.05          1    48
##  4 189         3072     2012     1 PR          0.05          2    47
##  5 189         3072     2012     1 PR          0.5           1    26
##  6 189         3072     2012     1 PR          0.5           2    24
##  7 189         3072     2012     1 PR          1             1    15
##  8 189         3072     2012     1 PR          1             2    13
##  9 189         3072     2012     1 PR          5             1     1
## 10 189         3072     2012     1 PR          5             2     0
## # … with 14 more rows
model_6=drm((GC)~Dose, data=g6, fct = W1.3())
plot(model_6)

summary(model_6)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.955422   0.231565  4.1259 0.0004808 ***
## d:(Intercept) 48.136272   1.933934 24.8903 < 2.2e-16 ***
## e:(Intercept)  0.659662   0.064261 10.2653 1.222e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  3.720013 (21 degrees of freedom)
ED(model_6,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50  0.44949    0.07144
mselect(model_6,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit   Res var
## W1.3    -63.98160 135.9632 4.707859e-01  13.83849
## W1.3    -63.98160 135.9632 4.707859e-01  13.83849
## W1.4    -63.96901 137.9380 2.952085e-01  14.51518
## LL2.3   -65.57769 139.1554 1.731262e-01  15.80714
## LL.3    -65.57769 139.1554 1.731262e-01  15.80714
## LL2.5   -64.01306 140.0261 1.183971e-01  15.33533
## W2.3    -66.03455 140.0691 1.279884e-01  16.42054
## LL.5    -64.12303 140.2461 1.069347e-01  15.47651
## BC.4    -65.24720 140.4944 1.131871e-01  16.14662
## LL.4    -65.49648 140.9930 9.388583e-02  16.48555
## LL2.4   -65.57752 141.1550 8.834906e-02  16.59727
## BC.5    -64.62795 141.2559 6.777163e-02  16.14161
## W2.4    -66.03331 142.0666 6.276848e-02  17.23980
## AR.2   -111.41550 228.8310 1.021459e-15 687.97966
## LL.2   -114.05675 234.1135 1.413257e-16 857.36490
## W1.2   -114.05801 234.1160 1.411929e-16 857.45455
## W2.2   -114.05801 234.1160 1.411929e-16 857.45455
## LL2.2  -114.05801 234.1160 1.411929e-16 857.45455
## LL.3u  -114.05601 236.1120 5.013408e-17 898.13642
## W1.3u  -114.05801 236.1160 5.005919e-17 898.28571
## LL2.3u -114.05801 236.1160 5.005919e-17 898.28571
## AR.3           NA       NA           NA        NA
## MM.2           NA       NA           NA        NA
## MM.3           NA       NA           NA        NA

“3708”

g7<-germination_assay %>% 
  filter(Isolate=="3708")
g7
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 247         3708     2014     1 PR          0             1    44
##  2 247         3708     2014     1 PR          0             2    47
##  3 247         3708     2014     1 PR          0.05          1    39
##  4 247         3708     2014     1 PR          0.05          2    42
##  5 247         3708     2014     1 PR          0.5           1     7
##  6 247         3708     2014     1 PR          0.5           2    13
##  7 247         3708     2014     1 PR          1             1     4
##  8 247         3708     2014     1 PR          1             2     7
##  9 247         3708     2014     1 PR          5             1     0
## 10 247         3708     2014     1 PR          5             2     0
## # … with 14 more rows
model_7=drm((GC)~Dose, data=g7, fct = W1.3())
plot(model_7)

summary(model_7)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.821554   0.073187  11.226 2.467e-10 ***
## d:(Intercept) 46.603848   1.232563  37.810 < 2.2e-16 ***
## e:(Intercept)  0.279406   0.026713  10.460 8.770e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  2.492597 (21 degrees of freedom)
ED(model_7,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.178849   0.020814
mselect(model_7,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit    Res var
## LL.3    -54.23715 116.4743 9.400930e-01   6.143635
## LL2.3   -54.23715 116.4743 9.400929e-01   6.143635
## W1.3    -54.37195 116.7439 8.945073e-01   6.213041
## W1.3    -54.37195 116.7439 8.945073e-01   6.213041
## W2.3    -54.90412 117.8082 6.989563e-01   6.494774
## BC.4    -54.11267 118.2253 9.032853e-01   6.384248
## LL.4    -54.13660 118.2732 8.872202e-01   6.396990
## LL2.4   -54.23665 118.4733 8.230819e-01   6.450548
## W1.4    -54.30320 118.6064 7.830076e-01   6.486422
## W2.4    -54.45979 118.9196 6.962449e-01   6.571618
## LL.5    -54.00746 120.0149 8.331776e-01   6.661596
## LL2.5   -54.00788 120.0158 8.320434e-01   6.661830
## BC.5    -54.12257 120.2451 6.449556e-01   6.725810
## AR.2   -109.20821 224.4164 1.014198e-17 572.388638
## LL.2   -110.29718 226.5944 4.485024e-18 626.761376
## W2.2   -110.29735 226.5947 4.484435e-18 626.770524
## W1.2   -110.29914 226.5983 4.478449e-18 626.863636
## LL2.2  -110.29914 226.5983 4.478449e-18 626.863636
## LL.3u  -110.29913 228.5983 1.584725e-18 656.714083
## W1.3u  -110.29914 228.5983 1.584721e-18 656.714286
## LL2.3u -110.29914 228.5983 1.584721e-18 656.714286
## AR.3           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA
## MM.3           NA       NA           NA         NA

“3712”

g8<-germination_assay %>% 
  filter(Isolate=="3712")
g8
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate  Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 233         3712     2014     1 PR          0             1    50
##  2 233         3712     2014     1 PR          0             2    48
##  3 233         3712     2014     1 PR          0.05          1    40
##  4 233         3712     2014     1 PR          0.05          2    37
##  5 233         3712     2014     1 PR          0.5           1     4
##  6 233         3712     2014     1 PR          0.5           2     3
##  7 233         3712     2014     1 PR          1             1     1
##  8 233         3712     2014     1 PR          1             2     2
##  9 233         3712     2014     1 PR          5             1     0
## 10 233         3712     2014     1 PR          5             2     0
## # … with 14 more rows
model_8=drm((GC)~Dose, data=g8, fct = W1.3())
plot(model_8)

summary(model_8)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.867186   0.089162  9.7259 3.145e-09 ***
## d:(Intercept) 49.348147   1.466895 33.6412 < 2.2e-16 ***
## e:(Intercept)  0.285151   0.030114  9.4690 4.993e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  2.968135 (21 degrees of freedom)
ED(model_8,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.186862   0.024135
mselect(model_8,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit    Res var
## W1.3    -58.56256 125.1251 9.546185e-01   8.809827
## W1.3    -58.56256 125.1251 9.546185e-01   8.809827
## LL2.3   -58.63324 125.2665 9.324557e-01   8.861868
## LL.3    -58.63324 125.2665 9.324557e-01   8.861868
## W2.3    -59.22835 126.4567 7.163762e-01   9.312429
## AR.3    -59.27966 126.5593 6.978334e-01   9.352337
## W1.4    -58.51731 127.0346 8.817144e-01   9.215504
## BC.4    -58.52272 127.0454 8.781462e-01   9.219657
## LL.4    -58.55565 127.1113 8.567244e-01   9.244992
## LL2.4   -58.63286 127.2657 8.085254e-01   9.304664
## W2.4    -58.89664 127.7933 6.633973e-01   9.511464
## BC.5    -58.36272 128.7254 8.893913e-01   9.576363
## LL.5    -58.36306 128.7261 8.879820e-01   9.576637
## LL2.5   -58.36646 128.7329 8.748592e-01   9.579351
## AR.2   -110.80434 227.6087 8.116812e-17 653.818206
## W2.2   -111.92859 229.8572 3.496548e-17 718.034096
## LL.2   -111.93030 229.8606 3.492075e-17 718.136356
## W1.2   -111.93030 229.8606 3.492074e-17 718.136364
## LL2.2  -111.93030 229.8606 3.492074e-17 718.136364
## LL.3u  -111.93030 231.8606 1.237007e-17 752.333333
## W1.3u  -111.93030 231.8606 1.237007e-17 752.333333
## LL2.3u -111.93030 231.8606 1.237007e-17 752.333333
## MM.2           NA       NA           NA         NA
## MM.3           NA       NA           NA         NA

“UEM-2628”

g9<-germination_assay %>% 
  filter(Isolate=="UEM-2628")
g9
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate   Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>    <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 153         UEM-2628  2011     1 PR          0             1    50
##  2 153         UEM-2628  2011     1 PR          0             2    49
##  3 153         UEM-2628  2011     1 PR          0.05          1    48
##  4 153         UEM-2628  2011     1 PR          0.05          2    41
##  5 153         UEM-2628  2011     1 PR          0.5           1     7
##  6 153         UEM-2628  2011     1 PR          0.5           2    11
##  7 153         UEM-2628  2011     1 PR          1             1     7
##  8 153         UEM-2628  2011     1 PR          1             2    10
##  9 153         UEM-2628  2011     1 PR          5             1     0
## 10 153         UEM-2628  2011     1 PR          5             2     0
## # … with 14 more rows
model_9=drm((GC)~Dose, data=g9, fct = LL.3())
plot(model_9)

summary(model_9)
## 
## Model fitted: Log-logistic (ED50 as parameter) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  1.115313   0.112801  9.8874 2.361e-09 ***
## d:(Intercept) 48.384085   1.585945 30.5080 < 2.2e-16 ***
## e:(Intercept)  0.202291   0.030039  6.7343 1.158e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  3.244151 (21 degrees of freedom)
ED(model_9,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.202291   0.030039
mselect(model_9,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit   Res var
## LL2.3   -60.69664 129.3933 3.256017e-01  10.52452
## LL.3    -60.69664 129.3933 3.256014e-01  10.52452
## LL.3    -60.69664 129.3933 3.256014e-01  10.52452
## W2.3    -60.84150 129.6830 2.974358e-01  10.65234
## W2.4    -59.86821 129.7364 3.446980e-01  10.31359
## LL.4    -60.54289 131.0858 2.078180e-01  10.91006
## BC.4    -60.55797 131.1159 2.054803e-01  10.92378
## LL2.4   -60.69623 131.3925 1.852401e-01  11.05037
## LL.5    -60.40669 132.8134 9.088920e-02  11.35466
## W1.3    -62.50581 133.0116 1.001883e-01  12.23710
## BC.5    -60.55547 133.1109 7.946619e-02  11.49632
## LL2.5   -60.66396 133.3279 7.211532e-02  11.60073
## W1.4    -62.44609 134.8922 4.986185e-02  12.78517
## AR.3    -64.82831 137.6566 2.017403e-02  14.85020
## LL.2   -112.07376 230.1475 3.376745e-17 726.77326
## W1.2   -112.07976 230.1595 3.361617e-17 727.13636
## W2.2   -112.07976 230.1595 3.361617e-17 727.13636
## LL2.2  -112.07976 230.1595 3.361617e-17 727.13636
## LL.3u  -112.07379 232.1476 1.196107e-17 761.38311
## W1.3u  -112.07976 232.1595 1.190768e-17 761.76190
## LL2.3u -112.07976 232.1595 1.190768e-17 761.76190
## AR.2           NA       NA           NA        NA
## MM.2           NA       NA           NA        NA
## MM.3           NA       NA           NA        NA

“UEM-3687”

g10<-germination_assay %>% 
  filter(Isolate=="UEM-3687")
g10
## # A tibble: 24 × 8
## # Groups:   Isolate, Dose, Replicates, Trail [24]
##    `Work Code` Isolate   Year Trail Population  Dose Replicates    GC
##    <chr>       <chr>    <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 201         UEM-3687  2013     1 PR          0             1    50
##  2 201         UEM-3687  2013     1 PR          0             2    50
##  3 201         UEM-3687  2013     1 PR          0.05          1    38
##  4 201         UEM-3687  2013     1 PR          0.05          2    39
##  5 201         UEM-3687  2013     1 PR          0.5           1     9
##  6 201         UEM-3687  2013     1 PR          0.5           2    12
##  7 201         UEM-3687  2013     1 PR          1             1     7
##  8 201         UEM-3687  2013     1 PR          1             2     5
##  9 201         UEM-3687  2013     1 PR          5             1     0
## 10 201         UEM-3687  2013     1 PR          5             2     0
## # … with 14 more rows
model_10=drm((GC)~Dose, data=g10, fct = W1.3())
plot(model_10)

summary(model_10)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.754332   0.048928  15.417 6.331e-13 ***
## d:(Intercept) 49.959061   0.920044  54.301 < 2.2e-16 ***
## e:(Intercept)  0.358266   0.023154  15.473 5.904e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  1.853094 (21 degrees of freedom)
ED(model_10,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.220390   0.018365
mselect(model_10,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(),BC.4(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit    Res var
## W1.3    -47.25671 102.5134 9.926256e-01   3.433958
## W1.3    -47.25671 102.5134 9.926256e-01   3.433958
## W1.4    -47.25348 104.5070 9.579036e-01   3.604685
## LL2.5   -47.28507 106.5701 7.186805e-01   3.804409
## LL.5    -47.43077 106.8615 5.584845e-01   3.850881
## BC.4    -49.13592 108.2718 2.334383e-01   4.216917
## LL.4    -49.17254 108.3451 2.271136e-01   4.229806
## LL.3    -50.70540 109.4108 1.445396e-01   4.577277
## LL2.3   -50.70540 109.4108 1.445396e-01   4.577277
## BC.5    -49.14231 110.2846 9.191991e-02   4.441225
## LL2.4   -50.70336 111.4067 7.204816e-02   4.805324
## W2.4    -50.80930 111.6186 6.654519e-02   4.847934
## AR.3    -53.64193 115.2839 1.926499e-02   5.846337
## W2.3    -54.21131 116.4226 1.289160e-02   6.130422
## W2.2   -112.40977 230.8195 5.712814e-21 747.410743
## LL.2   -112.41266 230.8253 5.700440e-21 747.590909
## W1.2   -112.41266 230.8253 5.700440e-21 747.590909
## LL2.2  -112.41266 230.8253 5.700440e-21 747.590909
## LL.3u  -112.41266 232.8253 2.012757e-21 783.190476
## W1.3u  -112.41266 232.8253 2.012757e-21 783.190476
## LL2.3u -112.41266 232.8253 2.012757e-21 783.190476
## AR.2           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA
## MM.3           NA       NA           NA         NA

EC50 Pyra

Calculation of Ec50 for the number of germinated conidia in relation to the dose, being stratified by year and population (RS, PR). The model was chosen through the tests performed above considering <IAC and best curve fit.

Model: W1.3

ec50_ger=estimate_EC50(GC~Dose,
                data= germination_assay ,
                isolate_col= "Isolate",
                strata_col = c("Year", "Population"),
                fct=drc::W1.3(),
                interval="delta")
ec50_ger
##          ID Year Population   Estimate  Std..Error      Lower      Upper
## 1   07Tr037 2007         RS 0.02859924 0.006744996 0.01457225 0.04262622
## 2   07Tr012 2007         RS 0.51282040 0.018645465 0.47404503 0.55159577
## 3   07Tr013 2007         RS 1.13393712 0.169768089 0.78088505 1.48698919
## 4   07Tr322 2007         RS 0.19047604 0.062835930 0.05980157 0.32115051
## 5   07Tr323 2007         RS 0.38690800 0.055745447 0.27097900 0.50283700
## 6   08Tr003 2008         RS 0.23496327 0.048103578 0.13492640 0.33500014
## 7   08Tr004 2008         RS 0.19360236 0.030907806 0.12932606 0.25787866
## 8   08Tr005 2008         RS 0.26564941 0.019285887 0.22554221 0.30575660
## 9   08Tr012 2008         RS 0.45700327 0.027791562 0.39920755 0.51479898
## 10  08Tr013 2008         RS 0.46222273 0.023727494 0.41287871 0.51156676
## 11  09Tr005 2009         RS 0.36533706 0.053042868 0.25502837 0.47564574
## 12  09Tr006 2009         RS 0.24903667 0.020650154 0.20609232 0.29198102
## 13  09Tr019 2009         RS 0.12990344 0.028674249 0.07027208 0.18953480
## 14  09Tr020 2009         RS 0.15498850 0.011726953 0.13060097 0.17937604
## 15  09Tr021 2009         RS 0.16999951 0.027364395 0.11309213 0.22690688
## 16  10Tr001 2010         RS 0.22621668 0.022299355 0.17984264 0.27259073
## 17  10Tr002 2010         RS 0.03229400 0.009516684 0.01250297 0.05208503
## 18  10Tr003 2010         RS 0.39109587 0.028861084 0.33107596 0.45111578
## 19  10Tr042 2010         RS 0.25283916 0.084760435 0.07657018 0.42910813
## 20  10Tr045 2010         RS 0.23207492 0.021314884 0.18774819 0.27640165
## 21  11Tr005 2011         RS 0.63981756 0.028947174 0.57961861 0.70001650
## 22  11Tr006 2011         RS 0.14485110 0.011232820 0.12149117 0.16821103
## 23  11Tr007 2011         RS 0.12785926 0.014061763 0.09861622 0.15710230
## 24  11Tr074 2011         RS 0.83486551 0.069172466 0.69101349 0.97871753
## 25  11Tr075 2011         RS 0.68746196 0.065458744 0.55133305 0.82359087
## 26 UEM-2580 2011         PR 0.38821005 0.071959286 0.23856252 0.53785758
## 27 UEM-2628 2011         PR 0.22961600 0.034565548 0.15773301 0.30149899
## 28 UEM-2665 2011         PR 0.42802281 0.043993249 0.33653385 0.51951178
## 29 UEM-2687 2011         PR 0.43148927 0.060709306 0.30523735 0.55774118
## 30 UEM-2703 2011         PR 0.16773016 0.013936273 0.13874810 0.19671223
## 31 UEM-2705 2011         PR 0.41416778 0.048163026 0.31400729 0.51432828
## 32     3066 2012         PR 0.66847601 0.032328476 0.60124527 0.73570676
## 33     3070 2012         PR 0.12496274 0.042235425 0.03712937 0.21279612
## 34     3071 2012         PR 0.12292312 0.018404328 0.08464923 0.16119701
## 35     3072 2012         PR 0.44949054 0.071440218 0.30092248 0.59805861
## 36     3279 2012         PR 0.50888777 0.039834388 0.42604762 0.59172792
## 37     3178 2012         PR 0.28989704 0.051394338 0.18301666 0.39677741
## 38 UEM-3687 2013         PR 0.22039011 0.018365067 0.18219787 0.25858236
## 39 UEM-3688 2013         PR 0.63878863 0.077013231 0.47863084 0.79894641
## 40 UEM-3696 2013         PR 0.23840711 0.092076947 0.04692262 0.42989161
## 41 UEM-3697 2013         PR 0.61996308 0.092059324 0.42851523 0.81141092
## 42     3393 2013         PR 0.64165091 0.038980033 0.56058749 0.72271433
## 43     3396 2013         PR 0.40536493 0.111277836 0.17395000 0.63677986
## 44     3705 2014         PR 0.23460145 0.085156071 0.05750970 0.41169319
## 45     3710 2014         PR 0.30173519 0.059343556 0.17832351 0.42514687
## 46     3711 2014         PR 0.12608417 0.032137041 0.05925154 0.19291681
## 47     3712 2014         PR 0.18686210 0.024134572 0.13667151 0.23705269
## 48     3713 2014         PR 0.21881656 0.037451567 0.14093176 0.29670136
## 49     3720 2014         PR 0.19968926 0.027838079 0.14179681 0.25758172
## 50     3708 2014         PR 0.17884923 0.020813614 0.13556495 0.22213351

Statistics

PARANÁ - PR

ec50_ger %>% 
  #filter(Population=="PR") %>% 
  summarise(mean=mean(Estimate),
            median=median(Estimate),
            sd=sd(Estimate),
            lower= quantile(Estimate, 0.25),
            upper= quantile(Estimate, 0.75))
##       mean    median       sd     lower     upper
## 1 0.338798 0.2509379 0.220033 0.1877656 0.4449902

RIO GRANDE DO SUL- RS

ec50_ger %>% 
  filter(Population=="RS") %>% 
  summarise(mean=mean(Estimate),
            median=median(Estimate),
            sd=sd(Estimate),
            lower= quantile(Estimate, 0.25),
            upper= quantile(Estimate, 0.75))
##        mean    median        sd     lower     upper
## 1 0.3401929 0.2490367 0.2608178 0.1699995 0.4570033

ALL ISOLATES

ec50_ger %>% 
  filter(Population=="PR") %>% 
  summarise(mean=mean(Estimate),
            median=median(Estimate),
            sd=sd(Estimate),
            lower= quantile(Estimate, 0.25),
            upper= quantile(Estimate, 0.75))
##       mean   median        sd     lower     upper
## 1 0.337403 0.289897 0.1755458 0.1996893 0.4314893

##Anova

aov_Ger<- lm(sqrt(Estimate)~Population,data= ec50_ger)
summary(aov_Ger)
## 
## Call:
## lm(formula = sqrt(Estimate) ~ Population, data = ec50_ger)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.37571 -0.12612 -0.04389  0.10501  0.52004 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   0.56204    0.03676  15.288   <2e-16 ***
## PopulationRS -0.01722    0.05199  -0.331    0.742    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1838 on 48 degrees of freedom
## Multiple R-squared:  0.002279,   Adjusted R-squared:  -0.01851 
## F-statistic: 0.1097 on 1 and 48 DF,  p-value: 0.742
Anova(aov_Ger)
## Anova Table (Type II tests)
## 
## Response: sqrt(Estimate)
##            Sum Sq Df F value Pr(>F)
## Population 0.0037  1  0.1097  0.742
## Residuals  1.6218 48

Homocedasticity

leveneTest(Estimate~Population,data=ec50_ger)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1  0.5741 0.4523
##       48

##Normality

shapiro.test(residuals(aov_Ger))
## 
##  Shapiro-Wilk normality test
## 
## data:  residuals(aov_Ger)
## W = 0.97012, p-value = 0.2342

Boxplot Ec50

box_pyra = ec50_total_pyra=ec50_ger %>% 
  filter(Estimate<100) %>% 
  ggplot(aes(Population, log(Estimate)))+
  ylim(-6, 6)+
  geom_jitter(size=2, color = "black", width = 0.05)+
  geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
  scale_color_colorblind()+
  #geom_hline(yintercept = .50, color="red")+
  #annotate("text", x=2.5,y=0.55, label="DD", size=4, color="red")+
  #facet_wrap("Population")+
  theme_minimal_hgrid()+
   labs(y = "log of EC50 ", 
        x="")
ggsave("img/box_PYRA.png", dpi = 500, height = 3.5, width = 5)

box_pyra

EC50 Azox

Data

This database contains information about all germination tests conducted, divided by: experiment and population.

germination_assay_azox<-  gsheet2tbl("https://docs.google.com/spreadsheets/d/1Mis_iDuVrq8x_IpjJDdhAeNPKHiIAZfea9sljgI1sIw/edit#gid=0") %>% 
  group_by(Isolate, Dose, Replicates, Trial) %>%
  dplyr::mutate(GC = as.numeric(GC)) %>%
  dplyr::mutate(Dose = as.numeric(Dose))
  
germination_assay_azox
## # A tibble: 700 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [700]
##    Code  Isolate  Year Trial Population  Dose Replicates    GC
##    <chr> <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT05 07Tr037  2007     1 RS         0              1    45
##  2 FGT05 07Tr037  2007     1 RS         0              2    49
##  3 FGT05 07Tr037  2007     1 RS         0.001          1    42
##  4 FGT05 07Tr037  2007     1 RS         0.001          2    42
##  5 FGT05 07Tr037  2007     1 RS         0.01           1    43
##  6 FGT05 07Tr037  2007     1 RS         0.01           2    40
##  7 FGT05 07Tr037  2007     1 RS         0.1            1    35
##  8 FGT05 07Tr037  2007     1 RS         0.1            2    35
##  9 FGT05 07Tr037  2007     1 RS         1              1    32
## 10 FGT05 07Tr037  2007     1 RS         1              2    30
## # … with 690 more rows

##Visualization

germination_assay_azox %>% 
  ggplot(aes(as.factor(Dose), GC, color=as.factor(Trial)))+
    stat_summary(fun.data = "mean_cl_boot",
               geom = "errorbar",
               width = 0,
               size =.5,
               position = position_dodge(width=.5))+
  stat_summary(fun = "mean",
               size = 1.4,
               geom = "point",
               position = position_dodge(width=.5))+
  facet_wrap(~Isolate)+
  scale_color_colorblind()+
  theme_minimal_hgrid(font_size = 6)+
   labs(x = expression(Dose~~μg/ml), y = "Number of germinated conidia/dose", color="")

##Models

Seven isolates were chosen to select the best model that fits all the data set.

Isolates: “07Tr012”

a1<-germination_assay_azox %>% 
  filter(Isolate=="07Tr012")
a1
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code  Isolate  Year Trial Population  Dose Replicates    GC
##    <chr> <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT06 07Tr012  2007     1 RS         0              1    50
##  2 FGT06 07Tr012  2007     1 RS         0              2    49
##  3 FGT06 07Tr012  2007     1 RS         0.001          1    48
##  4 FGT06 07Tr012  2007     1 RS         0.001          2    45
##  5 FGT06 07Tr012  2007     1 RS         0.01           1    45
##  6 FGT06 07Tr012  2007     1 RS         0.01           2    45
##  7 FGT06 07Tr012  2007     1 RS         0.1            1    42
##  8 FGT06 07Tr012  2007     1 RS         0.1            2    41
##  9 FGT06 07Tr012  2007     1 RS         1              1    39
## 10 FGT06 07Tr012  2007     1 RS         1              2    38
## # … with 18 more rows
az1=drm(GC~Dose, data=a1, fct = W2.3())
plot(az1)

summary(az1)
## 
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept) -0.192048   0.040655 -4.7238 7.623e-05 ***
## d:(Intercept) 49.264647   2.717077 18.1315 6.702e-16 ***
## e:(Intercept)  1.157380   0.923278  1.2536    0.2216    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  6.599493 (25 degrees of freedom)
ED(az1, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50   7.8037     5.6314
mselect(az1,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
##            logLik       IC  Lack of fit    Res var
## MM.3    -90.85473 189.7095 8.707204e-01   43.16696
## W1.3u   -90.95708 189.9142 8.433111e-01   43.48369
## W2.3    -90.97948 189.9590 8.371752e-01   43.55330
## W2.3    -90.97948 189.9590 8.371752e-01   43.55330
## AR.3    -91.35789 190.7158 7.295222e-01   44.74660
## LL2.3u  -91.45164 190.9033 7.025153e-01   45.04724
## LL.3u   -91.45164 190.9033 7.025151e-01   45.04724
## LL2.4   -90.47308 190.9462 8.892328e-01   43.75633
## LL.4    -90.47308 190.9462 8.892328e-01   43.75633
## LL2.3   -91.49766 190.9953 6.893097e-01   45.19557
## LL.3    -91.49766 190.9953 6.893097e-01   45.19557
## W1.4    -90.60163 191.2033 8.426163e-01   44.15996
## W2.4    -90.64875 191.2975 8.252081e-01   44.30883
## W1.3    -92.14782 192.2956 5.132524e-01   47.34394
## BC.5    -90.42954 192.8591 7.589586e-01   45.51701
## LL.5    -90.47147 192.9429 7.354639e-01   45.65353
## LL2.5   -90.47147 192.9429 7.354632e-01   45.65353
## W1.2   -141.39270 288.7854 5.574980e-16 1534.11538
## LL2.2  -141.39270 288.7854 5.574980e-16 1534.11538
## LL.2   -141.44315 288.8863 5.368665e-16 1539.65379
## W2.2   -141.50665 289.0133 5.119780e-16 1546.65374
## AR.2           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA

“09Tr019”

a2<-germination_assay_azox %>% 
  filter(Isolate=="09Tr019")
a2
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code  Isolate  Year Trial Population  Dose Replicates    GC
##    <chr> <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT73 09Tr019  2009     1 RS         0              1    40
##  2 FGT73 09Tr019  2009     1 RS         0              2    39
##  3 FGT73 09Tr019  2009     1 RS         0.001          1    33
##  4 FGT73 09Tr019  2009     1 RS         0.001          2    38
##  5 FGT73 09Tr019  2009     1 RS         0.01           1    33
##  6 FGT73 09Tr019  2009     1 RS         0.01           2    34
##  7 FGT73 09Tr019  2009     1 RS         0.1            1    33
##  8 FGT73 09Tr019  2009     1 RS         0.1            2    30
##  9 FGT73 09Tr019  2009     1 RS         1              1    25
## 10 FGT73 09Tr019  2009     1 RS         1              2    29
## # … with 18 more rows
az2=drm(GC~Dose, data=a2, fct = W1.3())
plot(az2)

ggsave("img/az2.png", dpi = 500)
## Saving 7 x 5 in image
summary(az2)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.289553   0.074216  3.9015 0.0006378 ***
## d:(Intercept) 44.723739   2.627931 17.0186 2.908e-15 ***
## e:(Intercept) 49.881337  27.458284  1.8166 0.0812845 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  6.515775 (25 degrees of freedom)
ED(az2, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50   14.067      8.234
mselect(az2,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
##            logLik       IC  Lack of fit    Res var
## W1.3    -90.62201 189.2440 9.992104e-01   42.45533
## W1.3    -90.62201 189.2440 9.992104e-01   42.45533
## LL2.3   -90.63175 189.2635 9.988879e-01   42.48487
## LL.3    -90.63175 189.2635 9.988879e-01   42.48487
## LL2.3u  -90.63971 189.2794 9.985847e-01   42.50904
## LL.3u   -90.63975 189.2795 9.985833e-01   42.50915
## W2.3    -90.98444 189.9689 9.574162e-01   43.56876
## W1.4    -90.59981 191.1996 9.975205e-01   44.15423
## LL2.4   -90.61070 191.2214 9.960268e-01   44.18858
## LL.4    -90.62179 191.2436 9.942947e-01   44.22359
## W2.4    -90.75671 191.5134 9.627263e-01   44.65186
## MM.3    -92.09497 192.1899 6.641055e-01   47.16555
## BC.5    -90.60529 193.2106 9.743879e-01   46.09202
## LL2.5   -90.60952 193.2190 9.713044e-01   46.10593
## LL.5    -90.62001 193.2400 9.636933e-01   46.14049
## AR.3    -93.43789 194.8758 3.428513e-01   51.91389
## LL.2   -139.23997 284.4799 4.073089e-15 1315.46154
## W1.2   -139.23997 284.4799 4.073089e-15 1315.46154
## LL2.2  -139.23997 284.4799 4.073089e-15 1315.46154
## W2.2   -139.35655 284.7131 3.733426e-15 1326.46152
## W1.3u          NA       NA           NA         NA
## AR.2           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA

“11Tr074”

a3<-germination_assay_azox %>% 
  filter(Isolate=="11Tr074")
a3
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code   Isolate  Year Trial Population  Dose Replicates    GC
##    <chr>  <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT133 11Tr074  2011     1 RS         0              1    43
##  2 FGT133 11Tr074  2011     1 RS         0              2    47
##  3 FGT133 11Tr074  2011     1 RS         0.001          1    22
##  4 FGT133 11Tr074  2011     1 RS         0.001          2    20
##  5 FGT133 11Tr074  2011     1 RS         0.01           1    16
##  6 FGT133 11Tr074  2011     1 RS         0.01           2    22
##  7 FGT133 11Tr074  2011     1 RS         0.1            1    20
##  8 FGT133 11Tr074  2011     1 RS         0.1            2    17
##  9 FGT133 11Tr074  2011     1 RS         1              1    16
## 10 FGT133 11Tr074  2011     1 RS         1              2    11
## # … with 18 more rows
az3=drm(GC~Dose, data=a3, fct = W1.3())
plot(az3)

summary(az3)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.156492   0.046572  3.3602  0.002502 ** 
## d:(Intercept) 46.513758   4.692831  9.9117 3.834e-10 ***
## e:(Intercept)  3.445858   3.962541  0.8696  0.392788    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  9.332128 (25 degrees of freedom)
ED(az3, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50  0.33125    0.46341
mselect(az3,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##           logLik       IC  Lack of fit   Res var
## W1.3   -100.6806 209.3613 9.635540e-01  87.08862
## W1.3   -100.6806 209.3613 9.635540e-01  87.08862
## LL2.3  -100.9568 209.9137 9.053661e-01  88.82370
## LL.3   -100.9568 209.9137 9.053654e-01  88.82372
## W2.3   -101.3578 210.7156 7.982989e-01  91.40455
## W1.3u  -101.4084 210.8169 7.838771e-01  91.73563
## LL2.4  -100.6044 211.2088 9.264313e-01  90.22454
## W1.4   -100.6131 211.2262 9.235396e-01  90.28064
## LL.4   -100.7269 211.4539 8.840873e-01  91.01785
## W2.4   -100.8201 211.6402 8.503173e-01  91.62544
## LL2.5  -100.6033 213.2067 7.972422e-01  94.14028
## LL.5   -100.6375 213.2749 7.770969e-01  94.37002
## BC.5   -100.6766 213.3532 7.546205e-01  94.63418
## LL2.3u -127.9633 263.9266 9.813056e-09 611.36000
## LL2.2  -134.1330 274.2659 2.547511e-10 913.38462
## W1.2   -134.1695 274.3389 2.479572e-10 915.76923
## LL.2   -134.2270 274.4540 2.376174e-10 919.53928
## W2.2   -134.3569 274.7139 2.158145e-10 928.11426
## LL.3u         NA       NA           NA        NA
## AR.2          NA       NA           NA        NA
## AR.3          NA       NA           NA        NA
## MM.2          NA       NA           NA        NA
## MM.3          NA       NA           NA        NA

“3070”

a4<-germination_assay_azox %>% 
  filter(Isolate=="3070")
a4
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code   Isolate  Year Trial Population  Dose Replicates    GC
##    <chr>  <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT187 3070     2012     1 PR         0              1    25
##  2 FGT187 3070     2012     1 PR         0              2    21
##  3 FGT187 3070     2012     1 PR         0.001          1    18
##  4 FGT187 3070     2012     1 PR         0.001          2    17
##  5 FGT187 3070     2012     1 PR         0.01           1    16
##  6 FGT187 3070     2012     1 PR         0.01           2    15
##  7 FGT187 3070     2012     1 PR         0.1            1    14
##  8 FGT187 3070     2012     1 PR         0.1            2    12
##  9 FGT187 3070     2012     1 PR         1              1     5
## 10 FGT187 3070     2012     1 PR         1              2     5
## # … with 18 more rows
az4=drm(GC~Dose, data=a4, fct = W1.3())
plot(az4)

summary(az4)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.62237    0.39968  1.5572    0.1320    
## d:(Intercept) 32.64611    4.10244  7.9577 2.588e-08 ***
## e:(Intercept)  0.46747    0.29805  1.5684    0.1293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  9.917212 (25 degrees of freedom)
ED(az4, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50  0.25942    0.20474
mselect(az4,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##           logLik       IC  Lack of fit   Res var
## W1.3   -102.3833 212.7666 9.832680e-01  98.35109
## W1.3   -102.3833 212.7666 9.832680e-01  98.35109
## LL2.3  -102.5096 213.0193 9.643266e-01  99.24274
## LL.3   -102.5096 213.0193 9.643252e-01  99.24280
## W2.3   -102.6285 213.2571 9.417458e-01 100.08921
## W1.4   -102.3826 214.7653 9.442207e-01 102.44423
## LL.4   -102.5080 215.0160 9.025729e-01 103.36586
## LL2.4  -102.5096 215.0192 9.020234e-01 103.37751
## W2.4   -102.6188 215.2375 8.630400e-01 104.18676
## LL2.5  -102.3897 216.7795 8.258859e-01 106.95266
## LL.5   -102.4473 216.8947 7.909700e-01 107.39356
## BC.5   -102.4986 216.9972 7.611464e-01 107.78739
## W1.2   -128.1994 262.3989 7.792768e-08 597.84615
## LL2.2  -128.2066 262.4133 7.751768e-08 598.15385
## LL.2   -128.2389 262.4777 7.571032e-08 599.53180
## W2.2   -128.2390 262.4780 7.570212e-08 599.53813
## W1.3u  -128.1994 264.3989 3.194539e-08 621.76000
## LL.3u  -128.1994 264.3989 3.194539e-08 621.76000
## LL2.3u -128.2066 264.4133 3.177596e-08 622.08000
## AR.2          NA       NA           NA        NA
## AR.3          NA       NA           NA        NA
## MM.2          NA       NA           NA        NA
## MM.3          NA       NA           NA        NA

“UEM-3688”

a5<-germination_assay_azox %>% 
  filter(Isolate=="UEM-3688")
a5
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code   Isolate   Year Trial Population  Dose Replicates    GC
##    <chr>  <chr>    <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT202 UEM-3688  2013     1 PR         0              1    49
##  2 FGT202 UEM-3688  2013     1 PR         0              2    50
##  3 FGT202 UEM-3688  2013     1 PR         0.001          1    48
##  4 FGT202 UEM-3688  2013     1 PR         0.001          2    47
##  5 FGT202 UEM-3688  2013     1 PR         0.01           1    47
##  6 FGT202 UEM-3688  2013     1 PR         0.01           2    45
##  7 FGT202 UEM-3688  2013     1 PR         0.1            1    46
##  8 FGT202 UEM-3688  2013     1 PR         0.1            2    43
##  9 FGT202 UEM-3688  2013     1 PR         1              1    43
## 10 FGT202 UEM-3688  2013     1 PR         1              2    40
## # … with 18 more rows
az5=drm(GC~Dose, data=a5, fct = W2.3())
plot(az5)

summary(az5)
## 
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept) -0.087403   0.027825 -3.1412   0.00429 ** 
## d:(Intercept) 47.732940   3.335571 14.3103 1.512e-13 ***
## e:(Intercept) 10.730341  19.075306  0.5625   0.57877    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  7.187007 (25 degrees of freedom)
ED(az5, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50   710.85    1585.98
mselect(az5,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [1]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
##            logLik       IC  Lack of fit    Res var
## W1.3u   -93.35943 194.7189 9.609340e-01   51.62378
## W2.3    -93.36737 194.7347 9.595087e-01   51.65307
## W2.3    -93.36737 194.7347 9.595087e-01   51.65307
## LL2.3u  -93.44986 194.8997 9.436143e-01   51.95831
## LL2.3   -93.45188 194.9038 9.432018e-01   51.96580
## W1.3    -93.50351 195.0070 9.323043e-01   52.15781
## LL.3u   -94.18050 196.3610 7.528002e-01   54.74195
## LL.3    -94.22697 196.4539 7.393993e-01   54.92393
## W2.4    -93.34562 196.6912 9.000683e-01   53.72174
## LL2.4   -93.40246 196.8049 8.799291e-01   53.94029
## LL.4    -93.40286 196.8057 8.797840e-01   53.94185
## W1.4    -93.43907 196.8781 8.667163e-01   54.08153
## MM.3    -94.75360 197.5072 5.907549e-01   57.02936
## AR.3    -95.07657 198.1531 5.069220e-01   58.36026
## BC.5    -93.09283 198.1857 9.086504e-01   55.05434
## LL2.5   -93.29833 198.5967 7.788620e-01   55.86843
## LL.5    -93.39543 198.7909 7.241544e-01   56.25729
## W1.2   -140.97806 287.9561 6.649486e-15 1489.34615
## LL2.2  -140.97806 287.9561 6.649486e-15 1489.34615
## LL.2   -141.02113 288.0423 6.439025e-15 1493.93513
## W2.2   -141.19622 288.3924 5.649864e-15 1512.73545
## AR.2           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA

“UEM-2580”

a6<-germination_assay_azox %>% 
  filter(Isolate=="UEM-2580")
a6
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code   Isolate   Year Trial Population  Dose Replicates    GC
##    <chr>  <chr>    <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT152 UEM-2580  2011     1 PR         0              1    45
##  2 FGT152 UEM-2580  2011     1 PR         0              2    44
##  3 FGT152 UEM-2580  2011     1 PR         0.001          1    42
##  4 FGT152 UEM-2580  2011     1 PR         0.001          2    43
##  5 FGT152 UEM-2580  2011     1 PR         0.01           1    40
##  6 FGT152 UEM-2580  2011     1 PR         0.01           2    42
##  7 FGT152 UEM-2580  2011     1 PR         0.1            1    37
##  8 FGT152 UEM-2580  2011     1 PR         0.1            2    37
##  9 FGT152 UEM-2580  2011     1 PR         1              1    21
## 10 FGT152 UEM-2580  2011     1 PR         1              2    27
## # … with 18 more rows
az6=drm(GC~Dose, data=a6, fct = W2.3())
plot(az6)

summary(az6)
## 
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##               Estimate Std. Error t-value   p-value    
## b:(Intercept) -0.27977    0.04879 -5.7342 5.655e-06 ***
## d:(Intercept) 46.11386    1.62593 28.3616 < 2.2e-16 ***
## e:(Intercept)  1.92918    0.86654  2.2263   0.03524 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  5.189595 (25 degrees of freedom)
ED(az6, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50   7.1501     2.9768
mselect(az6,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
##            logLik       IC  Lack of fit    Res var
## MM.3    -84.00091 176.0018 9.137416e-01   26.45689
## W1.3u   -84.21081 176.4216 8.608374e-01   26.85654
## W2.3    -84.25004 176.5001 8.503025e-01   26.93189
## W2.3    -84.25004 176.5001 8.503025e-01   26.93189
## W1.4    -83.48507 176.9701 9.836894e-01   26.56231
## LL2.4   -83.61199 177.2240 9.492979e-01   26.80420
## LL.4    -83.61199 177.2240 9.492979e-01   26.80420
## LL2.3u  -84.63713 177.2743 7.409016e-01   27.68695
## LL.3u   -84.63713 177.2743 7.409012e-01   27.68695
## LL2.3   -84.72136 177.4427 7.166056e-01   27.85403
## LL.3    -84.72136 177.4427 7.166054e-01   27.85403
## W2.4    -83.98056 177.9611 8.203410e-01   27.51922
## AR.3    -84.98880 177.9776 6.404033e-01   28.39123
## BC.5    -83.47761 178.9552 9.297348e-01   27.70242
## LL2.5   -83.48953 178.9791 9.214647e-01   27.72601
## LL.5    -83.52837 179.0567 8.950082e-01   27.80304
## W1.3    -85.71767 179.4353 4.529994e-01   29.90848
## W1.2   -140.56493 287.1299 6.997071e-18 1446.03846
## LL2.2  -140.56493 287.1299 6.997071e-18 1446.03846
## LL.2   -140.60657 287.2131 6.782383e-18 1450.34616
## W2.2   -140.66289 287.3258 6.502481e-18 1456.19223
## AR.2           NA       NA           NA         NA
## MM.2           NA       NA           NA         NA

“3711”

a7<-germination_assay_azox %>% 
  filter(Isolate=="3711")
a7
## # A tibble: 28 × 8
## # Groups:   Isolate, Dose, Replicates, Trial [28]
##    Code   Isolate  Year Trial Population  Dose Replicates    GC
##    <chr>  <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 FGT232 3711     2014     1 PR         0              1    48
##  2 FGT232 3711     2014     1 PR         0              2    48
##  3 FGT232 3711     2014     1 PR         0.001          1    47
##  4 FGT232 3711     2014     1 PR         0.001          2    46
##  5 FGT232 3711     2014     1 PR         0.01           1    45
##  6 FGT232 3711     2014     1 PR         0.01           2    44
##  7 FGT232 3711     2014     1 PR         0.1            1    44
##  8 FGT232 3711     2014     1 PR         0.1            2    42
##  9 FGT232 3711     2014     1 PR         1              1    15
## 10 FGT232 3711     2014     1 PR         1              2     9
## # … with 18 more rows
az7=drm(GC~Dose, data=a7, fct = W2.3())
plot(az7)

summary(az7)
## 
## Model fitted: Weibull (type 2) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept) -0.855739   0.199653 -4.2861 0.0002369 ***
## d:(Intercept) 40.844941   1.804982 22.6290 < 2.2e-16 ***
## e:(Intercept)  0.195578   0.053832  3.6331 0.0012626 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  6.258056 (25 degrees of freedom)
ED(az7, 50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50  0.30014    0.08533
mselect(az7,fctList =list(LL.2(),LL.3(), LL.3u(),LL.4(),LL.5(),W1.2(), W1.3(),W1.3u(), W1.4(),W2.4(), W2.3(), W2.2(), BC.5(),LL2.2(), LL2.3(), LL2.3u(),LL2.4(),LL2.5(), AR.2(), AR.3(), MM.2(), MM.3()))
## Warning in sqrt(diag(varMat)): NaNs produced

## Warning in sqrt(diag(varMat)): NaNs produced
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [2]
## Error in optim(startVec, opfct, hessian = TRUE, method = optMethod, control = list(maxit = maxIt,  : 
##   non-finite finite-difference value [3]
##            logLik       IC  Lack of fit   Res var
## W1.3    -89.22218 186.4444 6.984540e-01  38.41562
## LL.3    -89.33711 186.6742 6.656245e-01  38.73230
## LL2.3   -89.33711 186.6742 6.656245e-01  38.73230
## W2.3    -89.49203 186.9841 6.221527e-01  39.16327
## W2.3    -89.49203 186.9841 6.221527e-01  39.16327
## W1.4    -88.98268 187.9654 6.177169e-01  39.33755
## LL.4    -89.23166 188.4633 5.380240e-01  40.04339
## LL2.4   -89.33729 188.6746 5.065404e-01  40.34665
## W2.4    -89.42312 188.8462 4.820043e-01  40.59478
## LL2.5   -88.99636 189.9927 4.132803e-01  41.08801
## LL.5    -89.04649 190.0930 3.980330e-01  41.23537
## BC.5    -89.19959 190.3992 3.548527e-01  41.68881
## LL.3u  -117.68010 243.3602 1.924544e-09 293.29333
## LL2.2  -134.63384 275.2677 1.625564e-14 946.65385
## W1.2   -134.63725 275.2745 1.621429e-14 946.88462
## LL.2   -134.64296 275.2859 1.614535e-14 947.27081
## W2.2   -134.68040 275.3608 1.570038e-14 949.80747
## W1.3u  -134.63369 277.2674 6.276033e-15 984.51000
## LL2.3u -134.63384 277.2677 6.275366e-15 984.52000
## AR.2           NA       NA           NA        NA
## AR.3           NA       NA           NA        NA
## MM.2           NA       NA           NA        NA
## MM.3           NA       NA           NA        NA

EC50 Azox

Calculation of Ec50 for the number of germinated conidia in relation to the dose, being stratified by year and population (RS, PR). The model was chosen through the tests performed above considering <IAC and best curve fit.

Model: W1.3

ec50_ger_AZX=estimate_EC50(GC~Dose,
                data= germination_assay_azox ,
                isolate_col= "Isolate",
                strata_col = c("Year", "Population"),
                fct=drc::W1.3(),
                interval="delta")
ec50_ger_AZX
##          ID Year Population    Estimate   Std..Error         Lower        Upper
## 1   07Tr037 2007         RS  15.3972660   6.11655980  2.799975e+00   27.9945568
## 2   07Tr012 2007         RS   8.8559430   6.13352254 -3.776283e+00   21.4881692
## 3   07Tr013 2007         RS   0.3346700   0.19531041 -6.757936e-02    0.7369193
## 4   08Tr003 2008         RS   1.0944292   0.53585583 -9.186549e-03    2.1980449
## 5   08Tr013 2008         RS   2.9323113   1.63145054 -4.277240e-01    6.2923466
## 6   09Tr005 2009         RS   9.9565971   2.62264709  4.555154e+00   15.3580399
## 7   09Tr019 2009         RS  14.0673655   8.23396078 -2.890794e+00   31.0255252
## 8   10Tr002 2010         RS   9.9872003   4.27385204  1.185037e+00   18.7893634
## 9   10Tr003 2010         RS   0.5540930   0.22228724  9.628388e-02    1.0119022
## 10  11Tr005 2011         RS 277.9861111 184.72403078 -1.024602e+02  658.4323741
## 11  11Tr007 2011         RS   0.5677651   0.19580669  1.644936e-01    0.9710365
## 12  11Tr074 2011         RS   0.3312518   0.46340539 -6.231494e-01    1.2856531
## 13  11Tr075 2011         RS   4.0419176   2.58939046 -1.291032e+00    9.3748670
## 14 UEM-2580 2011         PR   8.2703330   3.74579346  5.557270e-01   15.9849391
## 15 UEM-2703 2011         PR   0.6131040   0.15327277  2.974329e-01    0.9287752
## 16 UEM-2705 2011         PR   2.5306268   0.53896788  1.420602e+00    3.6406519
## 17     3066 2012         PR   3.2252221   1.75083370 -3.806874e-01    6.8311316
## 18     3070 2012         PR   0.2594182   0.20473648 -1.622445e-01    0.6810808
## 19     3072 2012         PR  11.7019076   6.01149485 -6.789978e-01   24.0828130
## 20 UEM-3687 2013         PR   5.0744625   2.15384647  6.385326e-01    9.5103923
## 21 UEM-3688 2013         PR 268.0577834 394.45802381 -5.443437e+02 1080.4592909
## 22     3396 2013         PR   0.5257408   0.09866538  3.225357e-01    0.7289460
## 23     3705 2014         PR   0.7588132   0.27567321  1.910536e-01    1.3265728
## 24     3710 2014         PR   1.8818038   0.39278461  1.072849e+00    2.6907589
## 25     3711 2014         PR   0.3724231   0.12145307  1.222858e-01    0.6225604

Statistics

PARANÁ - PR

ec50_ger_AZX %>% 
  filter(Population=="PR") %>% 
  summarise(mean=mean(Estimate),
            median=median(Estimate),
            sd=sd(Estimate),
            lower= quantile(Estimate, 0.25),
            upper= quantile(Estimate, 0.75))
##       mean   median       sd     lower   upper
## 1 25.27264 2.206215 76.54076 0.5912632 5.87343

RIO GRANDE DO SUL - RS

ec50_ger_AZX %>% 
  filter(Population=="RS") %>% 
  summarise(mean=mean(Estimate),
            median=median(Estimate),
            sd=sd(Estimate),
            lower= quantile(Estimate, 0.25),
            upper= quantile(Estimate, 0.75))
##       mean   median       sd     lower  upper
## 1 26.62361 4.041918 75.71928 0.5677651 9.9872

ALL ISOLATES

ec50_ger_AZX %>% 
  summarise(mean=mean(Estimate),
            median=median(Estimate),
            sd=sd(Estimate), 
            lower_azox= quantile(Estimate, 0.25),
            upper_azox= quantile(Estimate, 0.75))
##       mean   median       sd lower_azox upper_azox
## 1 25.97514 2.932311 74.51389  0.5677651   9.956597

##Anova

aov_Ger_azox<- lm((Estimate)~Population,data= ec50_ger_AZX)
summary(aov_Ger_azox)
## 
## Call:
## lm(formula = (Estimate) ~ Population, data = ec50_ger_AZX)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -26.29 -24.90 -22.74 -16.67 251.36 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept)    25.273     21.972   1.150    0.262
## PopulationRS    1.351     30.470   0.044    0.965
## 
## Residual standard error: 76.11 on 23 degrees of freedom
## Multiple R-squared:  8.547e-05,  Adjusted R-squared:  -0.04339 
## F-statistic: 0.001966 on 1 and 23 DF,  p-value: 0.965
Anova(aov_Ger_azox)
## Anova Table (Type II tests)
## 
## Response: (Estimate)
##            Sum Sq Df F value Pr(>F)
## Population     11  1   0.002  0.965
## Residuals  133244 23

Homocedasticity

leveneTest(Estimate~Population,data=ec50_ger_AZX)
## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.
## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value Pr(>F)
## group  1   9e-04  0.977
##       23

##Normality

shapiro.test(residuals(aov_Ger_azox))
## 
##  Shapiro-Wilk normality test
## 
## data:  residuals(aov_Ger_azox)
## W = 0.36076, p-value = 2.033e-09

GLM

glm_azox = glm(Estimate ~ Population , family = Gamma (link=log), data  = ec50_ger_AZX )
summary(glm_azox)
## 
## Call:
## glm(formula = Estimate ~ Population, family = Gamma(link = log), 
##     data = ec50_ger_AZX)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.6793  -2.3955  -1.6224  -0.8456   3.8066  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   3.22972    0.84691   3.814 0.000893 ***
## PopulationRS  0.05208    1.17445   0.044 0.965015    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for Gamma family taken to be 8.60701)
## 
##     Null deviance: 107.32  on 24  degrees of freedom
## Residual deviance: 107.31  on 23  degrees of freedom
## AIC: 183.28
## 
## Number of Fisher Scoring iterations: 13

##Boxplot Ec50

box_azox_2=ec50_total_azox=ec50_ger_AZX %>% 
  ggplot(aes(Population, log(Estimate)))+
  geom_jitter(size=2, color = "black", width = 0.05)+
  ylim(-6,6)+
  geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
  scale_color_colorblind()+
   #geom_hline(yintercept = 5, color="red")+
  #annotate("text", x=2.5,y=5.5, label="DD", size=4, color="red")+
  #facet_wrap("Population")+
  theme_minimal_hgrid()+
   labs(y = "Log of EC50",
        x="")
ggsave("img/box_AZOX_2.png", dpi = 500, height = 3.5, width = 5)

box_azox_2

EC50 (both fungicides)

Here we loaded in the same data frame the EC50 values for both fungicides.

ec50_fun= gsheet2tbl("https://docs.google.com/spreadsheets/d/116vNPmNBOEoH0oWIFZxV9B8gkkv-wcYJ_fhbW2rpBZU/edit#gid=1135537345")

ec50_fun
## # A tibble: 50 × 7
##    `Work Code` Isolate State City         Year Fungicide      Estimate
##    <chr>       <chr>   <chr> <chr>       <dbl> <chr>             <dbl>
##  1 FGT05       07Tr037 RS    Cruz Alta    2007 Pyraclostrobin   0.0286
##  2 FGT06       07Tr012 RS    Cruz Alta    2007 Pyraclostrobin   0.513 
##  3 FGT07       07Tr013 RS    Cruz Alta    2007 Pyraclostrobin   1.13  
##  4 FGT33       08Tr003 RS    Santa Rosa   2008 Pyraclostrobin   0.235 
##  5 FGT43       08Tr013 RS    Giruá        2008 Pyraclostrobin   0.462 
##  6 FGT62       09Tr005 RS    Panambi      2009 Pyraclostrobin   0.365 
##  7 FGT73       09Tr019 RS    Cruz Alta    2009 Pyraclostrobin   0.130 
##  8 FGT92       10Tr002 RS    Passo Fundo  2010 Pyraclostrobin   0.0323
##  9 FGT93       10Tr003 RS    Passo Fundo  2010 Pyraclostrobin   0.391 
## 10 FGT125      11Tr005 RS    Ijuí         2011 Pyraclostrobin   0.640 
## # … with 40 more rows

Boxplot (azox and pyra)

box_azox_pyra <- ec50_fun %>% 
  filter(Estimate<100) %>% 
  ggplot(aes(Fungicide, log(Estimate)))+
  geom_boxplot(width=0.5, outlier.color = NA,  size =1)+
  ylim(-6, 6)+
  geom_jitter(size=2, color = "black",fill="black", width = 0.05)+
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank())+
  theme_minimal_hgrid()+
  scale_fill_few()+
   labs(x = "Fungicides",
        y= "Log of EC50")+
   theme(legend.position = "none")

Figure 2

(box_azox_2 | box_pyra)+
  plot_annotation(tag_levels = 'A')

ggsave("img/figure2.png", width = 8, height =5)

Correlation EC50

cor_ec50= gsheet2tbl("https://docs.google.com/spreadsheets/d/116vNPmNBOEoH0oWIFZxV9B8gkkv-wcYJ_fhbW2rpBZU/edit#gid=0") %>% 
  mutate(pyra=log(pyra)) %>% 
  mutate(azox=log(azox))
cor_ec50
## # A tibble: 25 × 7
##    `Work Code` Isolate State City         Year   pyra    azox
##    <chr>       <chr>   <chr> <chr>       <dbl>  <dbl>   <dbl>
##  1 FGT05       07Tr037 RS    Cruz Alta    2007 -3.55   2.73  
##  2 FGT06       07Tr012 RS    Cruz Alta    2007 -0.668  2.18  
##  3 FGT07       07Tr013 RS    Cruz Alta    2007  0.126 -1.09  
##  4 FGT33       08Tr003 RS    Santa Rosa   2008 -1.45   0.0902
##  5 FGT43       08Tr013 RS    Giruá        2008 -0.772  1.08  
##  6 FGT62       09Tr005 RS    Panambi      2009 -1.01   2.30  
##  7 FGT73       09Tr019 RS    Cruz Alta    2009 -2.04   2.64  
##  8 FGT92       10Tr002 RS    Passo Fundo  2010 -3.43   2.30  
##  9 FGT93       10Tr003 RS    Passo Fundo  2010 -0.939 -0.590 
## 10 FGT125      11Tr005 RS    Ijuí         2011 -0.447  5.80  
## # … with 15 more rows
cor.test((cor_ec50$azox), (cor_ec50$pyra), method ="spearman")
## 
##  Spearman's rank correlation rho
## 
## data:  (cor_ec50$azox) and (cor_ec50$pyra)
## S = 2552, p-value = 0.9311
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.01846154
cor_ec50 %>% 
  ggplot(aes(pyra, azox))+
  #geom_smooth(method=lm)+
  geom_point(shape=21, size=3)+
  theme_minimal()+
  labs(x =expression((Log)~EC[50]~~μg/ml~Pyraclostrobin), y = expression((Log)~EC[50]~~μg/ml~Azoxystrobin, color=""))

ggsave("img/cor_ec50_log.png", dpi = 500)
## Saving 7 x 5 in image

ISOLATE CML3066

We also calculated the EC50 for the isolates CML3066, which is a isolate from Brazil that has its genome sequenced.

EC50_P<- gsheet2tbl("https://docs.google.com/spreadsheets/d/1Mis_iDuVrq8x_IpjJDdhAeNPKHiIAZfea9sljgI1sIw/edit#gid=1606966266") %>% 
   filter(Code =="GEN-P") %>% 
   dplyr::mutate(GC = as.numeric(GC)) %>%
  dplyr::mutate(Dose = as.numeric(Dose)) 
 
  
EC50_P
## # A tibble: 24 × 8
##    Code  Isolate  Year Trial Population  Dose Replicates    GC
##    <chr> <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 GEN-P 09Tr125  2009     1 RS          0             1    45
##  2 GEN-P 09Tr125  2009     1 RS          0             2    37
##  3 GEN-P 09Tr125  2009     1 RS          0.05          1    30
##  4 GEN-P 09Tr125  2009     1 RS          0.05          2    30
##  5 GEN-P 09Tr125  2009     1 RS          0.5           1    16
##  6 GEN-P 09Tr125  2009     1 RS          0.5           2     5
##  7 GEN-P 09Tr125  2009     1 RS          1             1     9
##  8 GEN-P 09Tr125  2009     1 RS          1             2     2
##  9 GEN-P 09Tr125  2009     1 RS          5             1     0
## 10 GEN-P 09Tr125  2009     1 RS          5             2     0
## # … with 14 more rows

EC50 CML3066 Pyra

model_p=drm(GC~Dose, data=  EC50_P, fct = W1.3())
plot(model_p)

summary(model_p)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  0.635099   0.068349  9.2920 6.898e-09 ***
## d:(Intercept) 42.650786   1.456686 29.2793 < 2.2e-16 ***
## e:(Intercept)  0.250929   0.034610  7.2503 3.842e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  2.905047 (21 degrees of freedom)
ED(model_p,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50 0.140903   0.024111

EC50- CML3066 Azox

EC50_AZ<- gsheet2tbl("https://docs.google.com/spreadsheets/d/1Mis_iDuVrq8x_IpjJDdhAeNPKHiIAZfea9sljgI1sIw/edit#gid=1606966266") %>% 
   filter(Code =="GEN") %>% 
   dplyr::mutate(GC = as.numeric(GC)) %>%
  dplyr::mutate(Dose = as.numeric(Dose)) 
 
  
EC50_AZ
## # A tibble: 28 × 8
##    Code  Isolate  Year Trial Population  Dose Replicates    GC
##    <chr> <chr>   <dbl> <dbl> <chr>      <dbl>      <dbl> <dbl>
##  1 GEN   09Tr125  2009     1 RS         0              1    47
##  2 GEN   09Tr125  2009     1 RS         0              2    46
##  3 GEN   09Tr125  2009     1 RS         0.001          1    42
##  4 GEN   09Tr125  2009     1 RS         0.001          2    40
##  5 GEN   09Tr125  2009     1 RS         0.01           1    42
##  6 GEN   09Tr125  2009     1 RS         0.01           2    40
##  7 GEN   09Tr125  2009     1 RS         0.1            1    39
##  8 GEN   09Tr125  2009     1 RS         0.1            2    38
##  9 GEN   09Tr125  2009     1 RS         1              1     8
## 10 GEN   09Tr125  2009     1 RS         1              2     4
## # … with 18 more rows

EC50 CML3066 Azox

model_az=drm(GC~Dose, data=  EC50_AZ, fct = W1.3())
plot(model_az)

summary(model_az)
## 
## Model fitted: Weibull (type 1) with lower limit at 0 (3 parms)
## 
## Parameter estimates:
## 
##                Estimate Std. Error t-value   p-value    
## b:(Intercept)  1.031714   0.154052  6.6972 5.108e-07 ***
## d:(Intercept) 41.616353   1.062145 39.1814 < 2.2e-16 ***
## e:(Intercept)  0.480825   0.066215  7.2615 1.311e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error:
## 
##  3.305414 (25 degrees of freedom)
ED(model_az,50)
## 
## Estimated effective doses
## 
##        Estimate Std. Error
## e:1:50  0.33706    0.05393

Discriminatory Dose

We also screening isolates through discriminatory dose for each fungicide.

Load data RS

data_load_rs = gsheet2tbl("https://docs.google.com/spreadsheets/d/1xaHgZCiO85EwuowMlGKlvO1tlwG2SleaBG-L-5x4Enw/edit#gid=0") %>% 
  mutate(Fungicide = factor(Fungicide, levels = c("Control", "Azoxtrobin","Pyraclostrobin" )))
data_load_rs
## # A tibble: 1,890 × 12
##    Isolate `Original Code` City        lat  long  Year State Rpetition Trial
##      <dbl> <chr>           <chr>     <dbl> <dbl> <dbl> <chr>     <dbl> <dbl>
##  1       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            1     1
##  2       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            2     1
##  3       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            3     1
##  4       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            1     1
##  5       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            2     1
##  6       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            3     1
##  7       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            1     1
##  8       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            2     1
##  9       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            3     1
## 10       1 07Tr003         Cruz Alta -28.6 -53.6  2007 RS            1     2
## # … with 1,880 more rows, and 3 more variables: Fungicide <fct>, Dose <dbl>,
## #   conidia <dbl>

visualization

data_load_rs %>% 
  ggplot(aes(Fungicide,conidia))+
  geom_jitter()

## Single isolate

single_rs  = data_load_rs %>% 
  filter(Isolate == 1)  
single_rs %>% 
  ggplot(aes(Fungicide,conidia, color = factor(Trial)))+
  geom_jitter(height = 0)

Glm

glm_single = glm(conidia ~ Fungicide , family = poisson, data  = single_rs)
summary(glm_single)
## 
## Call:
## glm(formula = conidia ~ Fungicide, family = poisson, data = single_rs)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.96656  -0.16965   0.02360   0.07143   1.04939  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              3.90868    0.05783  67.587  < 2e-16 ***
## FungicideAzoxtrobin     -1.15715    0.11825  -9.786  < 2e-16 ***
## FungicidePyraclostrobin -0.83291    0.10506  -7.928 2.22e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 134.7694  on 17  degrees of freedom
## Residual deviance:   4.5796  on 15  degrees of freedom
## AIC: 102.07
## 
## Number of Fisher Scoring iterations: 4
coef(glm_single)[2:3]
##     FungicideAzoxtrobin FungicidePyraclostrobin 
##              -1.1571488              -0.8329091
confint(glm_single)
## Waiting for profiling to be done...
##                             2.5 %     97.5 %
## (Intercept)              3.793151  4.0199272
## FungicideAzoxtrobin     -1.393878 -0.9297843
## FungicidePyraclostrobin -1.041798 -0.6296018
azox_single = c(exp(coef(glm_single)[2]), exp(confint(glm_single)[2,]))
## Waiting for profiling to be done...
pyra_single = c(exp(coef(glm_single)[3]), exp(confint(glm_single)[3,]))
## Waiting for profiling to be done...

Data frame (GLM results)

isolates = unique(data_load_rs$Isolate)
azox_all = NULL
pyra_all = NULL

for(i in 1:length(isolates)){
single_df  = data_load_rs %>% 
  filter(Isolate == isolates[i]) 

glm_single = glm(conidia ~ Fungicide , family = poisson, data  = single_df)
summary(glm_single)

coef(glm_single)[2:3]
confint(glm_single)

azox_single = c(mean = as.numeric(exp(coef(glm_single)[2])), exp(confint(glm_single)[2,]), isol = isolates[i], year = unique(single_df$Year), local = unique(single_df$City),lat = unique(single_df$lat), long = unique(single_df$long), state= (single_df$State))
pyra_single = c(mean = as.numeric(exp(coef(glm_single)[3])), exp(confint(glm_single)[3,]), isol = isolates[i], year = unique(single_df$Year),local = unique(single_df$City), lat = unique(single_df$lat), long = unique(single_df$long), state= (single_df$State))

azox_all = rbind(azox_all, azox_single)
pyra_all = rbind(pyra_all, pyra_single)

}
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results_rs = bind_rows(
  
as.data.frame(azox_all) %>% 
  mutate(fungicide = "Azoxystrobin"),

as.data.frame(pyra_all) %>% 
  mutate(fungicide = "Pyraclostrobin")

)

Data frame reorganization

results_rs_1<-results_rs %>%  
summarise(mean=as.numeric(mean), 
         lower=as.numeric(`2.5 %`),
         upper= as.numeric(`97.5 %`),
        lat=as.numeric(lat),
        long=as.numeric(long), 
        isolate= as.numeric(isol),
        fungicide=fungicide, 
        year=year, 
        state=state1) 
results_rs_1
##            mean       lower      upper       lat      long isolate
## 1   0.314381271 0.248111164 0.39463883 -28.63940 -53.60620       1
## 2   0.259385666 0.200213323 0.33186732 -28.63940 -53.60620       2
## 3   0.290657440 0.226658790 0.36870679 -28.63940 -53.60620       3
## 4   0.089965398 0.058789031 0.13161526 -28.63940 -53.60620       4
## 5   0.347826087 0.277078704 0.43313620 -28.63940 -53.60620       5
## 6   0.555183946 0.458392220 0.67010477 -28.63940 -53.60620       8
## 7   0.632996633 0.526586071 0.75900598 -28.38100 -53.30600       9
## 8   0.343283582 0.259437041 0.44870866 -28.38100 -53.30600      10
## 9   0.531468531 0.435618457 0.64584799 -28.38100 -53.30600      11
## 10  0.529010239 0.434516269 0.64154635 -28.38100 -53.30600      12
## 11  0.355932203 0.283674486 0.44309205 -28.38100 -53.30600      15
## 12  0.020202020 0.007980190 0.04135790 -28.38100 -53.30600      16
## 13  0.662116041 0.551575867 0.79303448 -27.35570 -52.76600      17
## 14  0.636666667 0.530312961 0.76248546 -27.35570 -52.76600      18
## 15  0.283333333 0.221419757 0.35865532 -27.35570 -52.76600      19
## 16  0.071428571 0.042027703 0.11314237 -27.35570 -52.76600      21
## 17  0.531135531 0.433269543 0.64840999 -27.35570 -52.76600      22
## 18  0.208754209 0.157402862 0.27235822 -27.35570 -52.76600      23
## 19  0.364583333 0.290378142 0.45420850 -28.51070 -50.92980      26
## 20  0.397923875 0.319444229 0.49236335 -28.51070 -50.92980      30
## 21  0.601423488 0.496134104 0.72685347 -27.95000 -54.75000      31
## 22  0.137123746 0.097524091 0.18766164 -27.87100 -54.48200      32
## 23  0.620000000 0.515616328 0.74356310 -27.87100 -54.48200      36
## 24  0.580000000 0.480377162 0.69811667 -27.75760 -54.48510      37
## 25  0.728301887 0.604381183 0.87604649 -27.75760 -54.48510      38
## 26  0.493103448 0.402620718 0.60117879 -27.65730 -54.44390      40
## 27  0.670033670 0.559247209 0.80106015 -27.55480 -54.53610      41
## 28  0.410958904 0.331160296 0.50678245 -28.02830 -54.34970      44
## 29  0.032727273 0.015593213 0.05976616 -27.94250 -52.92360      45
## 30  0.153583618 0.110785772 0.20783331 -27.94250 -52.92360      46
## 31  0.749152542 0.628750725 0.89131112 -27.94250 -52.92360      47
## 32  0.294736842 0.229758718 0.37403712 -27.94250 -52.92360      48
## 33  0.358974359 0.271462157 0.46914675 -27.94250 -52.92360      49
## 34  0.698996656 0.585163254 0.83343233 -28.27830 -52.76650      50
## 35  0.153571429 0.109913055 0.20921473 -28.27830 -52.76650      51
## 36  0.288590604 0.225758565 0.36501205 -28.27830 -52.76650      52
## 37  0.040133779 0.021292682 0.06821920 -28.27830 -52.76650      53
## 38  0.580536913 0.480543499 0.69915793 -28.27830 -52.76650      54
## 39  0.273049645 0.210862667 0.34931599 -28.27830 -52.76650      55
## 40  0.326599327 0.258473932 0.40901823 -28.27830 -52.76650      56
## 41  0.500000000 0.409508110 0.60784659 -28.27830 -52.76650      57
## 42  0.503448276 0.411675736 0.61299689 -28.27830 -52.76650      58
## 43  0.376271186 0.301336801 0.46646229 -28.27830 -52.76650      59
## 44  0.386666667 0.310958088 0.47755512 -28.27830 -52.76650      60
## 45  0.313333333 0.247305269 0.39328412 -28.37000 -51.09200      61
## 46  0.423208191 0.341961185 0.52064028 -28.28990 -53.50170      65
## 47  0.567567568 0.468828830 0.68483486 -28.28990 -53.50170      66
## 48  0.562264151 0.459077673 0.68605139 -28.63940 -53.60620      67
## 49  0.858620690 0.724532923 1.01678259 -28.63940 -53.60620      68
## 50  0.337579618 0.245114013 0.45751868 -28.63940 -53.60620      70
## 51  0.133047211 0.089722211 0.19032930 -28.63940 -53.60620      71
## 52  0.211805556 0.159273358 0.27702897 -28.63940 -53.60620      76
## 53  0.759450172 0.637085218 0.90405389 -28.63940 -53.60620      78
## 54  0.630872483 0.524877690 0.75636344 -28.42220 -53.65570      80
## 55  0.231543624 0.176857961 0.29883407 -28.42220 -53.65570      81
## 56  0.072625699 0.039314811 0.12235111 -28.42220 -53.65570      82
## 57  0.315972222 0.248349011 0.39814721 -27.89940 -53.31360      83
## 58  0.731543624 0.613743313 0.87057714 -27.89940 -53.31360      84
## 59  0.234875445 0.178228025 0.30495359 -27.89940 -53.31360      85
## 60  0.155234657 0.111080389 0.21153495 -27.89940 -53.31360      86
## 61  0.138513514 0.098494919 0.18960568 -27.89940 -53.31360      87
## 62  0.388888889 0.297245961 0.50362552 -27.89940 -53.31360      88
## 63  0.194630872 0.145527749 0.25570165 -27.89940 -53.31360      89
## 64  0.017421603 0.006211844 0.03781776 -27.89940 -53.31360      90
## 65  0.510067114 0.418617264 0.61892855 -28.26120 -52.40830      94
## 66  0.166666667 0.115869953 0.23289533 -28.27830 -52.76650      95
## 67  0.501779359 0.408886725 0.61298874 -28.27830 -52.76650      96
## 68  0.328719723 0.259457333 0.41271027 -28.27830 -52.76650      97
## 69  0.396610169 0.319030814 0.48980083 -28.27830 -52.76650      99
## 70  0.151658768 0.102669603 0.21652111 -28.27830 -52.76650     100
## 71  0.650000000 0.542075610 0.77761800 -28.27830 -52.76650     101
## 72  0.770270270 0.647606457 0.91498394 -28.27830 -52.76650     102
## 73  0.335616438 0.265744468 0.42018626 -28.12840 -52.30280     105
## 74  0.367697595 0.293428221 0.45728252 -28.12840 -52.30280     106
## 75  0.314487633 0.246533386 0.39722458 -28.12840 -52.30280     107
## 76  0.207482993 0.156093008 0.27122880 -28.12840 -52.30280     108
## 77  0.707482993 0.591766698 0.84430150 -28.00840 -52.23610     109
## 78  0.512110727 0.419115013 0.62309837 -28.00840 -52.23610     110
## 79  0.468013468 0.381625808 0.57114337 -28.00840 -52.23610     111
## 80  0.385135135 0.309161411 0.47646789 -28.00840 -52.23610     112
## 81  0.638795987 0.532027498 0.76513149 -28.00840 -52.23610     113
## 82  0.709030100 0.594027352 0.84480766 -27.63050 -52.23640     114
## 83  0.394648829 0.317796031 0.48686972 -27.63050 -52.23640     115
## 84  0.041322314 0.020494409 0.07359827 -27.63050 -52.23640     116
## 85  0.744107744 0.624665011 0.88507292 -27.63050 -52.23640     120
## 86  0.871621622 0.737306460 1.02974718 -28.12840 -52.30280     121
## 87  0.073529412 0.045210235 0.11266134 -28.26120 -52.40830     123
## 88  0.543071161 0.442681604 0.66353683 -28.38850 -53.92050     128
## 89  0.536912752 0.442186445 0.64951390 -28.38850 -53.92050     129
## 90  0.741935484 0.619271687 0.88747496 -28.38850 -53.92050     130
## 91  0.721088435 0.603776941 0.85974010 -28.37000 -51.09200     131
## 92  0.840000000 0.710108859 0.99284112 -28.45790 -52.82200     132
## 93  0.620000000 0.515616328 0.74356310 -28.45790 -52.82200     135
## 94  0.619607843 0.507216032 0.75456828 -28.45790 -52.82200     136
## 95  0.335593220 0.266047401 0.41968656 -28.45790 -52.82200     137
## 96  0.874149660 0.739124765 1.03319167 -28.45790 -52.82200     138
## 97  0.747368421 0.625253927 0.89197343 -28.21070 -51.52620     140
## 98  0.738007380 0.614172540 0.88532101 -28.21070 -51.52620     141
## 99  0.498305085 0.407882591 0.60611175 -28.06480 -52.01000     142
## 100 0.770000000 0.648122841 0.91362745 -28.28990 -53.50170     143
## 101 0.360544218 0.287564115 0.44855797 -28.28990 -53.50170     144
## 102 0.140425533 0.095812511 0.19903864 -28.28990 -53.50170     145
## 103 0.043636364 0.023132560 0.07425957 -28.37067 -52.98822     146
## 104 0.811447811 0.684228423 0.96135545 -28.64086 -52.85092     147
## 105 0.118644068 0.082144461 0.16590833 -28.28990 -53.50170     148
## 106 0.434782609 0.352819659 0.53280393 -28.63940 -53.60620       1
## 107 0.225255973 0.171092096 0.29212999 -28.63940 -53.60620       2
## 108 0.259515571 0.199957457 0.33257009 -28.63940 -53.60620       3
## 109 0.193771626 0.144129068 0.25574035 -28.63940 -53.60620       4
## 110 0.214046823 0.162048693 0.27830647 -28.63940 -53.60620       5
## 111 0.421404682 0.341133937 0.51750371 -28.63940 -53.60620       8
## 112 0.464646465 0.378679001 0.56729787 -28.38100 -53.30600       9
## 113 0.169154229 0.115641450 0.23978190 -28.38100 -53.30600      10
## 114 0.601398601 0.496959108 0.72562498 -28.38100 -53.30600      11
## 115 0.416382253 0.336010387 0.51281938 -28.38100 -53.30600      12
## 116 0.461016949 0.375238773 0.56352926 -28.38100 -53.30600      15
## 117 0.006734007 0.001116400 0.02092764 -28.38100 -53.30600      16
## 118 0.546075085 0.449491235 0.66100059 -27.35570 -52.76600      17
## 119 0.633333333 0.527373030 0.75870159 -27.35570 -52.76600      18
## 120 0.140000000 0.099958436 0.19099255 -27.35570 -52.76600      19
## 121 0.016806723 0.005186625 0.03947496 -27.35570 -52.76600      21
## 122 0.315018315 0.245876773 0.39949584 -27.35570 -52.76600      22
## 123 0.245791246 0.188937294 0.31553930 -27.35570 -52.76600      23
## 124 0.347222222 0.275342590 0.43421176 -28.51070 -50.92980      26
## 125 0.391003460 0.313428478 0.48441510 -28.51070 -50.92980      30
## 126 0.441281139 0.356123169 0.54364846 -27.95000 -54.75000      31
## 127 0.093645485 0.062169463 0.13530847 -27.87100 -54.48200      32
## 128 0.546666667 0.451050393 0.66020543 -27.87100 -54.48200      36
## 129 0.406666667 0.328392090 0.50046396 -27.75760 -54.48510      37
## 130 0.649056604 0.534936670 0.78546216 -27.75760 -54.48510      38
## 131 0.268965517 0.208140170 0.34340821 -27.65730 -54.44390      40
## 132 0.538720539 0.443628298 0.65177929 -27.55480 -54.53610      41
## 133 0.277397260 0.215561487 0.35290535 -28.02830 -54.34970      44
## 134 0.054545455 0.031030726 0.08839099 -27.94250 -52.92360      45
## 135 0.160409556 0.116463451 0.21592734 -27.94250 -52.92360      46
## 136 0.542372881 0.446540472 0.65635766 -27.94250 -52.92360      47
## 137 0.333333333 0.263000995 0.41868446 -27.94250 -52.92360      48
## 138 0.343589746 0.258558395 0.45087475 -27.94250 -52.92360      49
## 139 0.688963211 0.576301362 0.82205479 -28.27830 -52.76650      50
## 140 0.167857143 0.121762366 0.22619934 -28.27830 -52.76650      51
## 141 0.053691275 0.031148535 0.08575199 -28.27830 -52.76650      52
## 142 0.026755853 0.012138979 0.05037308 -28.27830 -52.76650      53
## 143 0.412751678 0.333460242 0.50777534 -28.27830 -52.76650      54
## 144 0.138297872 0.097460344 0.19075644 -28.27830 -52.76650      55
## 145 0.377104377 0.302291309 0.46708554 -28.27830 -52.76650      56
## 146 0.435810811 0.353339581 0.53452154 -28.27830 -52.76650      57
## 147 0.362068966 0.288430645 0.45097599 -28.27830 -52.76650      58
## 148 0.030508475 0.014544293 0.05566517 -28.27830 -52.76650      59
## 149 0.300000000 0.235787602 0.37790654 -28.27830 -52.76650      60
## 150 0.410000000 0.331300369 0.50427949 -28.37000 -51.09200      61
## 151 0.317406143 0.250111564 0.39902859 -28.28990 -53.50170      65
## 152 0.466216216 0.379919908 0.56928028 -28.28990 -53.50170      66
## 153 0.664150943 0.548152247 0.80272827 -28.63940 -53.60620      67
## 154 0.486206897 0.396586736 0.59329737 -28.63940 -53.60620      68
## 155 0.388535032 0.287051234 0.51916688 -28.63940 -53.60620      70
## 156 0.145922747 0.100080298 0.20600753 -28.63940 -53.60620      71
## 157 0.131944444 0.092617138 0.18253650 -28.63940 -53.60620      76
## 158 0.701030928 0.585529508 0.83773880 -28.63940 -53.60620      78
## 159 0.624161074 0.518962215 0.74874029 -28.42220 -53.65570      80
## 160 0.251677852 0.194059467 0.32224854 -28.42220 -53.65570      81
## 161 0.122905028 0.076766315 0.18693078 -28.42220 -53.65570      82
## 162 0.263888889 0.203604940 0.33779436 -27.89940 -53.31360      83
## 163 0.587248322 0.486450293 0.70678977 -27.89940 -53.31360      84
## 164 0.313167260 0.245179371 0.39601923 -27.89940 -53.31360      85
## 165 0.054151625 0.030809176 0.08774271 -27.89940 -53.31360      86
## 166 0.158783784 0.115305504 0.21368787 -27.89940 -53.31360      87
## 167 0.166666667 0.113299023 0.23734491 -27.89940 -53.31360      88
## 168 0.161073826 0.117344920 0.21619117 -27.89940 -53.31360      89
## 169 0.059233450 0.034934962 0.09351700 -27.89940 -53.31360      90
## 170 0.459731544 0.374509452 0.56149661 -28.26120 -52.40830      94
## 171 0.063063063 0.035047709 0.10408108 -28.27830 -52.76650      95
## 172 0.466192171 0.377826707 0.57222298 -28.27830 -52.76650      96
## 173 0.200692042 0.149962524 0.26387091 -28.27830 -52.76650      97
## 174 0.223728814 0.169958010 0.29009670 -28.27830 -52.76650      99
## 175 0.118483412 0.076380843 0.17564405 -28.27830 -52.76650     100
## 176 0.690000000 0.577390008 0.82298918 -28.27830 -52.76650     101
## 177 0.699324324 0.584923533 0.83454256 -28.27830 -52.76650     102
## 178 0.301369863 0.236178003 0.38065633 -28.12840 -52.30280     105
## 179 0.316151203 0.248819670 0.39788720 -28.12840 -52.30280     106
## 180 0.162544170 0.117558590 0.21955036 -28.12840 -52.30280     107
## 181 0.037414966 0.019254584 0.06492699 -28.12840 -52.30280     108
## 182 0.489795918 0.400288661 0.59659782 -28.00840 -52.23610     109
## 183 0.550173010 0.452476072 0.66653921 -28.00840 -52.23610     110
## 184 0.319865320 0.252649368 0.40125817 -28.00840 -52.23610     111
## 185 0.418918919 0.338595290 0.51518856 -28.00840 -52.23610     112
## 186 0.525083612 0.431940506 0.63583815 -28.00840 -52.23610     113
## 187 0.618729097 0.514336039 0.74234407 -27.63050 -52.23640     114
## 188 0.260869565 0.202023406 0.33278270 -27.63050 -52.23640     115
## 189 0.107438018 0.070011137 0.15775239 -27.63050 -52.23640     116
## 190 0.518518519 0.425891643 0.62876212 -27.63050 -52.23640     120
## 191 0.611486486 0.507473253 0.73479125 -28.12840 -52.30280     121
## 192 0.058823529 0.034090904 0.09408423 -28.26120 -52.40830     123
## 193 0.516853933 0.419825898 0.63345294 -28.38850 -53.92050     128
## 194 0.543624161 0.448083118 0.65715586 -28.38850 -53.92050     129
## 195 0.655913978 0.543616632 0.78950129 -28.38850 -53.92050     130
## 196 0.663265306 0.552761836 0.79409761 -28.37000 -51.09200     131
## 197 0.853333333 0.721924336 1.00792082 -28.45790 -52.82200     132
## 198 0.640000000 0.533253188 0.76626903 -28.45790 -52.82200     135
## 199 0.643137255 0.527747078 0.78157080 -28.45790 -52.82200     136
## 200 0.220338983 0.167076637 0.28613931 -28.45790 -52.82200     137
## 201 0.435374150 0.352702946 0.53438803 -28.45790 -52.82200     138
## 202 0.736842105 0.615973810 0.88001097 -28.21070 -51.52620     140
## 203 0.627306273 0.517038578 0.75895162 -28.21070 -51.52620     141
## 204 0.410169492 0.330842912 0.50534377 -28.06480 -52.01000     142
## 205 0.873333333 0.739652273 1.03053564 -28.28990 -53.50170     143
## 206 0.462585034 0.376476760 0.56551197 -28.28990 -53.50170     144
## 207 0.114893617 0.075418998 0.16780557 -28.28990 -53.50170     145
## 208 0.061818182 0.036441035 0.09766599 -28.37067 -52.98822     146
## 209 0.787878788 0.663372275 0.93466552 -28.64086 -52.85092     147
## 210 0.189830508 0.141258263 0.25040876 -28.28990 -53.50170     148
##          fungicide year state
## 1     Azoxystrobin 2007    RS
## 2     Azoxystrobin 2007    RS
## 3     Azoxystrobin 2007    RS
## 4     Azoxystrobin 2007    RS
## 5     Azoxystrobin 2007    RS
## 6     Azoxystrobin 2007    RS
## 7     Azoxystrobin 2007    RS
## 8     Azoxystrobin 2007    RS
## 9     Azoxystrobin 2007    RS
## 10    Azoxystrobin 2007    RS
## 11    Azoxystrobin 2007    RS
## 12    Azoxystrobin 2007    RS
## 13    Azoxystrobin 2007    RS
## 14    Azoxystrobin 2007    RS
## 15    Azoxystrobin 2007    RS
## 16    Azoxystrobin 2007    RS
## 17    Azoxystrobin 2007    RS
## 18    Azoxystrobin 2007    RS
## 19    Azoxystrobin 2007    RS
## 20    Azoxystrobin 2007    RS
## 21    Azoxystrobin 2008    RS
## 22    Azoxystrobin 2008    RS
## 23    Azoxystrobin 2008    RS
## 24    Azoxystrobin 2008    RS
## 25    Azoxystrobin 2008    RS
## 26    Azoxystrobin 2008    RS
## 27    Azoxystrobin 2008    RS
## 28    Azoxystrobin 2008    RS
## 29    Azoxystrobin 2008    RS
## 30    Azoxystrobin 2008    RS
## 31    Azoxystrobin 2008    RS
## 32    Azoxystrobin 2008    RS
## 33    Azoxystrobin 2008    RS
## 34    Azoxystrobin 2008    RS
## 35    Azoxystrobin 2008    RS
## 36    Azoxystrobin 2008    RS
## 37    Azoxystrobin 2008    RS
## 38    Azoxystrobin 2008    RS
## 39    Azoxystrobin 2008    RS
## 40    Azoxystrobin 2008    RS
## 41    Azoxystrobin 2008    RS
## 42    Azoxystrobin 2008    RS
## 43    Azoxystrobin 2008    RS
## 44    Azoxystrobin 2008    RS
## 45    Azoxystrobin 2009    RS
## 46    Azoxystrobin 2009    RS
## 47    Azoxystrobin 2009    RS
## 48    Azoxystrobin 2009    RS
## 49    Azoxystrobin 2009    RS
## 50    Azoxystrobin 2009    RS
## 51    Azoxystrobin 2009    RS
## 52    Azoxystrobin 2009    RS
## 53    Azoxystrobin 2009    RS
## 54    Azoxystrobin 2009    RS
## 55    Azoxystrobin 2009    RS
## 56    Azoxystrobin 2009    RS
## 57    Azoxystrobin 2009    RS
## 58    Azoxystrobin 2009    RS
## 59    Azoxystrobin 2009    RS
## 60    Azoxystrobin 2009    RS
## 61    Azoxystrobin 2009    RS
## 62    Azoxystrobin 2009    RS
## 63    Azoxystrobin 2009    RS
## 64    Azoxystrobin 2009    RS
## 65    Azoxystrobin 2010    RS
## 66    Azoxystrobin 2010    RS
## 67    Azoxystrobin 2010    RS
## 68    Azoxystrobin 2010    RS
## 69    Azoxystrobin 2010    RS
## 70    Azoxystrobin 2010    RS
## 71    Azoxystrobin 2010    RS
## 72    Azoxystrobin 2010    RS
## 73    Azoxystrobin 2010    RS
## 74    Azoxystrobin 2010    RS
## 75    Azoxystrobin 2010    RS
## 76    Azoxystrobin 2010    RS
## 77    Azoxystrobin 2010    RS
## 78    Azoxystrobin 2010    RS
## 79    Azoxystrobin 2010    RS
## 80    Azoxystrobin 2010    RS
## 81    Azoxystrobin 2010    RS
## 82    Azoxystrobin 2010    RS
## 83    Azoxystrobin 2010    RS
## 84    Azoxystrobin 2010    RS
## 85    Azoxystrobin 2010    RS
## 86    Azoxystrobin 2010    RS
## 87    Azoxystrobin 2011    RS
## 88    Azoxystrobin 2011    RS
## 89    Azoxystrobin 2011    RS
## 90    Azoxystrobin 2011    RS
## 91    Azoxystrobin 2011    RS
## 92    Azoxystrobin 2011    RS
## 93    Azoxystrobin 2011    RS
## 94    Azoxystrobin 2011    RS
## 95    Azoxystrobin 2011    RS
## 96    Azoxystrobin 2011    RS
## 97    Azoxystrobin 2011    RS
## 98    Azoxystrobin 2011    RS
## 99    Azoxystrobin 2011    RS
## 100   Azoxystrobin 2011    RS
## 101   Azoxystrobin 2011    RS
## 102   Azoxystrobin 2011    RS
## 103   Azoxystrobin 2011    RS
## 104   Azoxystrobin 2011    RS
## 105   Azoxystrobin 2011    RS
## 106 Pyraclostrobin 2007    RS
## 107 Pyraclostrobin 2007    RS
## 108 Pyraclostrobin 2007    RS
## 109 Pyraclostrobin 2007    RS
## 110 Pyraclostrobin 2007    RS
## 111 Pyraclostrobin 2007    RS
## 112 Pyraclostrobin 2007    RS
## 113 Pyraclostrobin 2007    RS
## 114 Pyraclostrobin 2007    RS
## 115 Pyraclostrobin 2007    RS
## 116 Pyraclostrobin 2007    RS
## 117 Pyraclostrobin 2007    RS
## 118 Pyraclostrobin 2007    RS
## 119 Pyraclostrobin 2007    RS
## 120 Pyraclostrobin 2007    RS
## 121 Pyraclostrobin 2007    RS
## 122 Pyraclostrobin 2007    RS
## 123 Pyraclostrobin 2007    RS
## 124 Pyraclostrobin 2007    RS
## 125 Pyraclostrobin 2007    RS
## 126 Pyraclostrobin 2008    RS
## 127 Pyraclostrobin 2008    RS
## 128 Pyraclostrobin 2008    RS
## 129 Pyraclostrobin 2008    RS
## 130 Pyraclostrobin 2008    RS
## 131 Pyraclostrobin 2008    RS
## 132 Pyraclostrobin 2008    RS
## 133 Pyraclostrobin 2008    RS
## 134 Pyraclostrobin 2008    RS
## 135 Pyraclostrobin 2008    RS
## 136 Pyraclostrobin 2008    RS
## 137 Pyraclostrobin 2008    RS
## 138 Pyraclostrobin 2008    RS
## 139 Pyraclostrobin 2008    RS
## 140 Pyraclostrobin 2008    RS
## 141 Pyraclostrobin 2008    RS
## 142 Pyraclostrobin 2008    RS
## 143 Pyraclostrobin 2008    RS
## 144 Pyraclostrobin 2008    RS
## 145 Pyraclostrobin 2008    RS
## 146 Pyraclostrobin 2008    RS
## 147 Pyraclostrobin 2008    RS
## 148 Pyraclostrobin 2008    RS
## 149 Pyraclostrobin 2008    RS
## 150 Pyraclostrobin 2009    RS
## 151 Pyraclostrobin 2009    RS
## 152 Pyraclostrobin 2009    RS
## 153 Pyraclostrobin 2009    RS
## 154 Pyraclostrobin 2009    RS
## 155 Pyraclostrobin 2009    RS
## 156 Pyraclostrobin 2009    RS
## 157 Pyraclostrobin 2009    RS
## 158 Pyraclostrobin 2009    RS
## 159 Pyraclostrobin 2009    RS
## 160 Pyraclostrobin 2009    RS
## 161 Pyraclostrobin 2009    RS
## 162 Pyraclostrobin 2009    RS
## 163 Pyraclostrobin 2009    RS
## 164 Pyraclostrobin 2009    RS
## 165 Pyraclostrobin 2009    RS
## 166 Pyraclostrobin 2009    RS
## 167 Pyraclostrobin 2009    RS
## 168 Pyraclostrobin 2009    RS
## 169 Pyraclostrobin 2009    RS
## 170 Pyraclostrobin 2010    RS
## 171 Pyraclostrobin 2010    RS
## 172 Pyraclostrobin 2010    RS
## 173 Pyraclostrobin 2010    RS
## 174 Pyraclostrobin 2010    RS
## 175 Pyraclostrobin 2010    RS
## 176 Pyraclostrobin 2010    RS
## 177 Pyraclostrobin 2010    RS
## 178 Pyraclostrobin 2010    RS
## 179 Pyraclostrobin 2010    RS
## 180 Pyraclostrobin 2010    RS
## 181 Pyraclostrobin 2010    RS
## 182 Pyraclostrobin 2010    RS
## 183 Pyraclostrobin 2010    RS
## 184 Pyraclostrobin 2010    RS
## 185 Pyraclostrobin 2010    RS
## 186 Pyraclostrobin 2010    RS
## 187 Pyraclostrobin 2010    RS
## 188 Pyraclostrobin 2010    RS
## 189 Pyraclostrobin 2010    RS
## 190 Pyraclostrobin 2010    RS
## 191 Pyraclostrobin 2010    RS
## 192 Pyraclostrobin 2011    RS
## 193 Pyraclostrobin 2011    RS
## 194 Pyraclostrobin 2011    RS
## 195 Pyraclostrobin 2011    RS
## 196 Pyraclostrobin 2011    RS
## 197 Pyraclostrobin 2011    RS
## 198 Pyraclostrobin 2011    RS
## 199 Pyraclostrobin 2011    RS
## 200 Pyraclostrobin 2011    RS
## 201 Pyraclostrobin 2011    RS
## 202 Pyraclostrobin 2011    RS
## 203 Pyraclostrobin 2011    RS
## 204 Pyraclostrobin 2011    RS
## 205 Pyraclostrobin 2011    RS
## 206 Pyraclostrobin 2011    RS
## 207 Pyraclostrobin 2011    RS
## 208 Pyraclostrobin 2011    RS
## 209 Pyraclostrobin 2011    RS
## 210 Pyraclostrobin 2011    RS

Boxplot (all isolates)

box_1=results_rs_1 %>% 
  ggplot(aes(fungicide, mean), color=lower>0.5)+
  ylim(0,1)+
  geom_jitter(size=2, width = 0.2, color="gray")+
  geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
  scale_color_colorblind()+
   geom_hline(yintercept = 0.5, color="red")+
  #annotate("text", x=2.5,y=5.5, label="DD", size=4, color="red")+
  #facet_wrap("Population")+
  theme_minimal()+
   labs(y = "",
        x="")

box_1

#ggsave("img/box_AZOX.png", dpi = 500, height = 3.5, width = 5)

Less sensitive year, Pyra, RS

results_rs_2 <- results_rs_1 %>% 
  filter(fungicide== "Pyraclostrobin")

dd_1=results_rs_2 %>% 
  ggplot(aes(year, mean, group=lower, color=lower>0.5))+
  geom_hline(yintercept = .50, color="red")+
  scale_color_colorblind()+
  scale_y_continuous(limits = c(0, 1.2))+
  geom_jitter(position= position_dodge(width = 1))+
  geom_errorbar(aes(ymin = lower, ymax = upper, color = lower>0.5), width=1, position= position_dodge(width = 1), size=0.3)+
  #facet_wrap(~fungicide, ncol = 1)+
theme_minimal(base_size = 18)+
   labs(x="", y="", color="LL IC95")+
  theme(legend.position = "none")


  
dd_1

ggsave("img/dd_rs_2.png",dpi= 600, height = 8, width = 8)

Less sensitive, year, Azox, RS

results_rs_3 <- results_rs_1 %>% 
  filter(fungicide== "Azoxystrobin")

dd_2=results_rs_3 %>% 
  ggplot(aes(year, mean, group=lower, color=lower>0.5))+
  geom_hline(yintercept = .50, color="red")+
  scale_color_colorblind()+
  scale_y_continuous(limits = c(0, 1.2))+
  geom_jitter(position= position_dodge(width = 1))+
  geom_errorbar(aes(ymin = lower, ymax = upper, color = lower>0.5), width=1, position= position_dodge(width = 1), size=0.3)+
  theme_minimal(base_size = 18)+
  labs(x="Year", y="", color="LL IC95")+
  theme(legend.position = "none")

  
dd_2

Proportion by year

results_freq<-results_rs_1 %>% 
group_by( fungicide, year) %>% 
  mutate(n_isolates=length(isolate)) %>% 
  filter(lower>0.5) %>% 
  summarise(count=n(), 
            prop=(count/mean(n_isolates))*100)
## `summarise()` has grouped output by 'fungicide'. You can override using the `.groups` argument.
results_freq
## # A tibble: 10 × 4
## # Groups:   fungicide [2]
##    fungicide      year  count  prop
##    <chr>          <chr> <int> <dbl>
##  1 Azoxystrobin   2007      3 15   
##  2 Azoxystrobin   2008      5 20.8 
##  3 Azoxystrobin   2009      4 20   
##  4 Azoxystrobin   2010      7 31.8 
##  5 Azoxystrobin   2011     10 52.6 
##  6 Pyraclostrobin 2007      1  5   
##  7 Pyraclostrobin 2008      2  8.33
##  8 Pyraclostrobin 2009      3 15   
##  9 Pyraclostrobin 2010      4 18.2 
## 10 Pyraclostrobin 2011      9 47.4

Less sensitive, year, RS

dd_2+dd_1+plot_annotation(tag_levels = list(c("A", "B")))+plot_layout(ncol = 2)+
  labs(y= "")

ggsave("img/dd_all_all.png",dpi= 500, height = 8, width = 14)

Load data - Paraná

data_load_pr = gsheet2tbl("https://docs.google.com/spreadsheets/d/1xaHgZCiO85EwuowMlGKlvO1tlwG2SleaBG-L-5x4Enw/edit#gid=29082389") %>% 
  mutate(Fungicide = factor(Fungicide, levels = c("Control", "Azoxtrobin","Pyraclostrobin" ))) %>% 
  filter(!is.na(conidia))
data_load_pr
## # A tibble: 1,332 × 9
##    Isolate  Year Host  State Repetição Trial Fungicide       Dose conidia
##      <dbl> <dbl> <chr> <chr>     <dbl> <dbl> <fct>          <dbl>   <dbl>
##  1    6001  2018 Wheat PR            1     1 Control          0        50
##  2    6001  2018 Wheat PR            2     1 Control          0        41
##  3    6001  2018 Wheat PR            3     1 Control          0        46
##  4    6001  2018 Wheat PR            1     1 Pyraclostrobin   0.5      25
##  5    6001  2018 Wheat PR            2     1 Pyraclostrobin   0.5      25
##  6    6001  2018 Wheat PR            3     1 Pyraclostrobin   0.5      22
##  7    6001  2018 Wheat PR            1     1 Azoxtrobin       5        41
##  8    6001  2018 Wheat PR            2     1 Azoxtrobin       5        42
##  9    6001  2018 Wheat PR            3     1 Azoxtrobin       5        43
## 10    6014  2018 Wheat PR            1     1 Control          0        40
## # … with 1,322 more rows

visualization

data_load_pr %>% 
  ggplot(aes(Fungicide,conidia))+
  geom_jitter()

Single isolate

single_pr  = data_load_pr %>% 
  filter(Isolate == 6040)  
single_pr %>% 
  ggplot(aes(Fungicide,conidia, color = factor(Trial)))+
  geom_jitter(height = 0)

Glm

glm_single_pr = glm(conidia ~ Fungicide , family = poisson, data  = single_pr)
summary(glm_single_pr)
## 
## Call:
## glm(formula = conidia ~ Fungicide, family = poisson, data = single_pr)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.9446  -0.7474  -0.2246   0.5318   2.8338  
## 
## Coefficients:
##                         Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              3.83586    0.05998  63.957  < 2e-16 ***
## FungicideAzoxtrobin     -0.43466    0.09567  -4.543 5.54e-06 ***
## FungicidePyraclostrobin -1.62029    0.14758 -10.979  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for poisson family taken to be 1)
## 
##     Null deviance: 189.736  on 17  degrees of freedom
## Residual deviance:  25.854  on 15  degrees of freedom
## AIC: 121.04
## 
## Number of Fisher Scoring iterations: 4
coef(glm_single_pr)[2:3]
##     FungicideAzoxtrobin FungicidePyraclostrobin 
##              -0.4346643              -1.6202879
confint(glm_single_pr)
## Waiting for profiling to be done...
##                              2.5 %     97.5 %
## (Intercept)              3.7159586  3.9511505
## FungicideAzoxtrobin     -0.6235703 -0.2482721
## FungicidePyraclostrobin -1.9194786 -1.3398278
azox_single_pr = c(exp(coef(glm_single_pr)[2]), exp(confint(glm_single_pr)[2,]))
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pyra_single_pr = c(exp(coef(glm_single_pr)[3]), exp(confint(glm_single_pr)[3,]))
## Waiting for profiling to be done...

Data frame Paraná

isolates = unique(data_load_pr$Isolate)
azox_all_pr = NULL
pyra_all_pr = NULL

for(i in 1:length(isolates)){
single_df_pr  = data_load_pr %>% 
  filter(Isolate == isolates[i]) 

glm_single_pr = glm(conidia ~ Fungicide , family = poisson, data  = single_df_pr)
summary(glm_single_pr)

# coef(glm_single_pr)[2:3]
# confint(glm_single_pr)

azox_single_pr = c(mean = as.numeric(exp(coef(glm_single_pr)[2])), exp(confint(glm_single_pr)[2,]), isol = isolates[i], year = unique(single_df_pr$Year), state= (single_df_pr$State))
pyra_single_pr = c(mean = as.numeric(exp(coef(glm_single_pr)[3])), exp(confint(glm_single_pr)[3,]), isol = isolates[i], year = unique(single_df_pr$Year), state= (single_df_pr$State))

azox_all_pr = rbind(azox_all_pr, azox_single_pr)
pyra_all_pr = rbind(pyra_all_pr, pyra_single_pr)

}
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## Warning in rbind(azox_all_pr, azox_single_pr): number of columns of result is
## not a multiple of vector length (arg 2)
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## Warning in rbind(azox_all_pr, azox_single_pr): number of columns of result is
## not a multiple of vector length (arg 2)

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results_pr = bind_rows(
  
as.data.frame(azox_all_pr) %>% 
  mutate(fungicide = "Azoxystrobin"),

as.data.frame(pyra_all_pr) %>% 
  mutate(fungicide = "Pyraclostrobin")

)
results_pr
##                                 mean               2.5 %             97.5 %
## azox_single_pr     0.736462093862819   0.614049760285394  0.881814217349013
## azox_single_pr.1   0.727611940298508   0.604412481562192  0.874354426807635
## azox_single_pr.2   0.175675675675676   0.129429060728632  0.233641642151195
## azox_single_pr.3   0.732441471571906   0.614718463267838  0.871341884812485
## azox_single_pr.4                0.56   0.462776514457351  0.675374512904487
## azox_single_pr.5   0.647482014388492   0.536027214974515  0.780147627398873
## azox_single_pr.6   0.193979933110368   0.145051146235219   0.25482503265163
## azox_single_pr.7   0.144278606965175  0.0957742505671896  0.209259086915127
## azox_single_pr.8   0.667785234899332   0.557434017627934  0.798269538468759
## azox_single_pr.9   0.474747474747475   0.387521110641593  0.578832684123637
## azox_single_pr.10  0.302083333333335   0.236384539726069    0.3820858411134
## azox_single_pr.11  0.315068493150685   0.247989654076369  0.396483238995149
## azox_single_pr.12               0.34   0.270396327333935  0.423983553387451
## azox_single_pr.13  0.329931972789304   0.261041866866901  0.413320194140721
## azox_single_pr.14  0.585284280936456    0.48487599560576  0.704339320490915
## azox_single_pr.15  0.224489796117301   0.170523167011626  0.291109800244756
## azox_single_pr.16  0.560402684563758   0.462831991284609  0.676253555502486
## azox_single_pr.17  0.724489795918395   0.606780106052922  0.863599141636898
## azox_single_pr.18  0.434931506849315   0.352058998137144  0.534251184295637
## azox_single_pr.19  0.321070234113712   0.253895834887262  0.402347145739479
## azox_single_pr.20   0.65979381443299   0.549181943089545  0.790883428764812
## azox_single_pr.21  0.503448275922299   0.411675735982017  0.612996885821208
## azox_single_pr.22  0.783505154639176   0.658333147162011   0.93134099094598
## azox_single_pr.23  0.280155642023346   0.214255465133445  0.361651800819593
## azox_single_pr.24  0.447098976286417   0.362810883451566  0.547991498972251
## azox_single_pr.25  0.181184669168706   0.133404495677388  0.241151545939536
## azox_single_pr.26  0.308474576271187   0.242606670194475   0.38841719076349
## azox_single_pr.27  0.213559322033898   0.161320993023581  0.278217416081666
## azox_single_pr.28  0.282229965156794   0.219223023262169  0.359234776330337
## azox_single_pr.29  0.270758122780608   0.208403376761355  0.347404629887614
## azox_single_pr.30  0.218309859160396    0.16444596695745  0.285162092033507
## azox_single_pr.31   0.48135593220339   0.393036280033232  0.586764289192992
## azox_single_pr.32  0.417391304347827    0.32756133966138  0.527727167413352
## azox_single_pr.33  0.925423728813559   0.784763966067886   1.09091259640605
## azox_single_pr.34               0.22   0.167187555846748   0.28513503826905
## azox_single_pr.35  0.759999999999999   0.639274441802317  0.902304466289692
## azox_single_pr.36  0.328859060402814   0.260533719815264  0.411468805730865
## azox_single_pr.37  0.367892976588629   0.294507893053458  0.456185949936581
## azox_single_pr.38  0.430976430980662   0.349243948133924  0.528809852375624
## azox_single_pr.39  0.555183946488294   0.458392220254799  0.670104770263569
## azox_single_pr.40  0.608108108108108    0.50449849805326  0.730950563032375
## azox_single_pr.41  0.418060200668897   0.338214189992631  0.513676968913334
## azox_single_pr.42  0.177852349129064   0.131394672092216   0.23598806795972
## azox_single_pr.43  0.411552346570399   0.329738135746453  0.510273158493164
## azox_single_pr.44  0.138047138047152  0.0981691681758914  0.188953208994231
## azox_single_pr.45               0.11  0.0753783343018091  0.155057772265107
## azox_single_pr.46  0.432885906040269   0.351037194286136  0.530814311884507
## azox_single_pr.47  0.109215017066751  0.0743883195542293  0.154713895579635
## azox_single_pr.48 0.0166666666666667 0.00594857537715076 0.0361144096059694
## azox_single_pr.49  0.441767068273094   0.356835408979862  0.544419731321839
## azox_single_pr.50  0.685810810810811    0.57299580900304  0.819208502323291
## azox_single_pr.51  0.797153024911032   0.668338910374552  0.949690024337805
## azox_single_pr.52  0.633333333334409   0.527373029681431  0.758701592633516
## azox_single_pr.53  0.353333333333334   0.281967024718939  0.439308095772046
## azox_single_pr.54  0.636015325670499    0.52273948633875  0.771662508741244
## azox_single_pr.55 0.0833333333377186   0.054028347535519  0.122611963536443
## azox_single_pr.56 0.0761245677127294   0.047930071764579  0.114542240708587
## azox_single_pr.57  0.296666666666667   0.232911344778924  0.374058987089619
## azox_single_pr.58  0.406666666666672   0.328392090108229  0.500463962713472
## azox_single_pr.59  0.364548494983344   0.291600716373095   0.45234663757886
## azox_single_pr.60  0.811447811447812   0.684228422949569  0.961355449646873
## azox_single_pr.61  0.796666666666666   0.671727141350079  0.943813512325582
## azox_single_pr.62   0.64429530201383   0.536712290670529  0.771606084631782
## azox_single_pr.63  0.557046979865772   0.459881414936977  0.672434817308839
## azox_single_pr.64  0.783333333333334   0.659923466582632  0.928722007888093
## azox_single_pr.65  0.665467625899281   0.551823569518132  0.800655244543632
## azox_single_pr.66  0.526666666666667   0.433473452375284   0.63743955996646
## azox_single_pr.67   0.84452296819788    0.71056753769233   1.00289384702309
## azox_single_pr.68  0.643884892093517   0.532868964557929  0.776045083115291
## azox_single_pr.69  0.595959595959597   0.493962132019483    0.7169146078664
## azox_single_pr.70  0.800000000023582   0.674678523553356   0.94758592474469
## azox_single_pr.71  0.555183946488365   0.458392220255716  0.670104770263335
## azox_single_pr.72  0.157718120805369   0.114546255128577  0.212220499527221
## azox_single_pr.73  0.730038022819985   0.605473123726237   0.87864260629676
## pyra_single_pr     0.530685920577618   0.433524056641728  0.646966697324173
## pyra_single_pr.1   0.690298507462687   0.571671607273097  0.831752266406569
## pyra_single_pr.2   0.206081081081081   0.155060951397095  0.269348939709096
## pyra_single_pr.3   0.759197324414722   0.638378728523427  0.901653425596519
## pyra_single_pr.4   0.586666666666667   0.486246959136405   0.70569447219772
## pyra_single_pr.5   0.197841726635927   0.146683418156368  0.261890771204113
## pyra_single_pr.6  0.0167224080267635 0.00596386019674012 0.0362855415792789
## pyra_single_pr.7   0.114427860696518  0.0723638609628605  0.172195907938272
## pyra_single_pr.8   0.684563758389269   0.572243476943708  0.817306634109355
## pyra_single_pr.9   0.377104377115135   0.302291309036699  0.467085539870741
## pyra_single_pr.10  0.322916666666674   0.254339139464398  0.406170002422291
## pyra_single_pr.11 0.0650684931570514  0.0395305492391583  0.100525745864662
## pyra_single_pr.12  0.296666666666667   0.232911344779023  0.374058987089516
## pyra_single_pr.13   0.27891156473307    0.21705231822163  0.354376333391454
## pyra_single_pr.14  0.655518394648864   0.546778556698634  0.784112819677388
## pyra_single_pr.15 0.0714285714840541  0.0444894908660828  0.108333084064155
## pyra_single_pr.16  0.687919463087248   0.575206154578319  0.821113267198319
## pyra_single_pr.17  0.710884353741496   0.594768891575958  0.848161513475645
## pyra_single_pr.18  0.493150684931507   0.402945794346117  0.600828140228273
## pyra_single_pr.19  0.110367892976596  0.0756264525527886  0.155586774736329
## pyra_single_pr.20  0.570446735395189   0.470583786955688  0.689208506647174
## pyra_single_pr.21  0.541379310350691   0.444916104383383  0.656291250740088
## pyra_single_pr.22  0.810996563573884   0.682628794760469  0.962513943049958
## pyra_single_pr.23 0.0622568093385724  0.0360558988709022 0.0996713283206131
## pyra_single_pr.24  0.167235494880546   0.122157678649661   0.22400482926375
## pyra_single_pr.25  0.313588850174216   0.246188457207935   0.39554916560224
## pyra_single_pr.26  0.457627118644068   0.372274549607327  0.559654731530144
## pyra_single_pr.27   0.23728813559322    0.18150574839368  0.305904036788069
## pyra_single_pr.28  0.383275261324041   0.306472641723166  0.475886750407023
## pyra_single_pr.29  0.350180505415162   0.276616487075066  0.439511365579735
## pyra_single_pr.30  0.316901408450704   0.248720492420781  0.399856535060752
## pyra_single_pr.31  0.691525423728813   0.577862686711556  0.825940455851763
## pyra_single_pr.32  0.313043481227553   0.238677243174476  0.405579841225468
## pyra_single_pr.33  0.854237288135594   0.721703115153595   1.01035983367146
## pyra_single_pr.34  0.173333333333335   0.127737307193651  0.230451845882701
## pyra_single_pr.35  0.533333333333333   0.439330745849071  0.645029870424608
## pyra_single_pr.36   0.25838926176187   0.199808230838154   0.33003843671734
## pyra_single_pr.37   0.19732441591913   0.147877449610057  0.258745225855751
## pyra_single_pr.38  0.289562289562289   0.226499795810115  0.366276912244224
## pyra_single_pr.39  0.204013378132844   0.153538225616476  0.266577445217289
## pyra_single_pr.40  0.503378378378378   0.412469800305448   0.61170035475248
## pyra_single_pr.41  0.147157190635452   0.105826729844388  0.199598488281592
## pyra_single_pr.42  0.288590604026846   0.225758564933675  0.365012051856034
## pyra_single_pr.43    0.4476534296029   0.361108729674641  0.551775149240801
## pyra_single_pr.44  0.262626262626263   0.203351379000158  0.335086798833649
## pyra_single_pr.45 0.0600000000000001  0.0359536956875244 0.0936119474050601
## pyra_single_pr.46  0.140939597315853   0.100617293385768  0.192302813256822
## pyra_single_pr.47  0.204778156996587   0.153726253685399  0.268180689624605
## pyra_single_pr.48  0.103333333333333  0.0701415921423287  0.146588356815575
## pyra_single_pr.49  0.297858099062918   0.232667217410031  0.377795732999322
## pyra_single_pr.50   0.52027027027027   0.427285532633516  0.630961873201428
## pyra_single_pr.51  0.729537366548043   0.608754078392752  0.872806058623015
## pyra_single_pr.52  0.476666666666729   0.389602661011539   0.58045330613946
## pyra_single_pr.53  0.403333335054656   0.325484538262465  0.496647707619392
## pyra_single_pr.54  0.586206896551724   0.479240537599833  0.714562817726239
## pyra_single_pr.55 0.0666666666666988  0.0410416126438434  0.101974352757514
## pyra_single_pr.56   0.19031141869715   0.141216932988057  0.251670478739371
## pyra_single_pr.57  0.113333333333362  0.0780844841910115  0.159075646453773
## pyra_single_pr.58  0.393333333336048   0.316766356114976  0.485194467507685
## pyra_single_pr.59  0.237458193979933   0.181987205065638  0.305593432869177
## pyra_single_pr.60  0.750841750841753   0.630617685685966   0.89270484067356
## pyra_single_pr.61  0.696666666666792   0.583279362081604  0.830547418347796
## pyra_single_pr.62   0.61744966442953   0.513047993779011  0.741115883423909
## pyra_single_pr.63  0.644295302013423   0.536712290670316  0.771606084631106
## pyra_single_pr.64   0.67666666666803   0.565614316943365  0.807869680183281
## pyra_single_pr.65  0.485611510791367   0.394322422780966   0.59511652061376
## pyra_single_pr.66  0.523333333333333   0.430545457097313   0.63364375309989
## pyra_single_pr.67  0.908127208480565   0.766758162618815   1.07506746319497
## pyra_single_pr.68  0.586330935252567   0.482387444429798  0.710353870406725
## pyra_single_pr.69  0.555555555555556   0.458420648710462  0.670948460090695
## pyra_single_pr.70  0.576666666667606   0.477442799432558  0.694327231222852
## pyra_single_pr.71  0.515050167224369   0.423131037692264  0.624408170954716
## pyra_single_pr.72  0.187919463243494   0.139865355263224  0.247825378721152
## pyra_single_pr.73  0.600760456272149   0.492320780506628   0.73071113536962
##                   isol year state1 state2 state3 state4 state5 state6 state7
## azox_single_pr    6001 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.1  6014 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.2  6015 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.3  6020 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.4  6022 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.5  6040 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.6  6041 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.7  6045 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.8  6054 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.9  6055 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.10 6057 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.11 6064 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.12 6069 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.13 6070 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.14 6071 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.15 6074 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.16 6090 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.17 6094 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.18 6100 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.19 6111 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.20 6112 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.21 6124 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.22 6132 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.23 6133 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.24 6145 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.25 6149 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.26 6150 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.27 6162 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.28 6171 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.29 6179 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.30 6181 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.31 6190 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.32 6193 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.33 6201 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.34 6204 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.35 6225 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.36 6229 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.37 6240 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.38 6241 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.39 6250 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.40 6253 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.41 6270 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.42 6273 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.43 6280 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.44 6281 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.45 6284 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.46 6285 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.47 6291 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.48 6297 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.49 6299 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.50 6302 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.51 6318 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.52 6336 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.53 6341 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.54 6350 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.55 6352 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.56 6357 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.57 6360 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.58 6374 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.59 6376 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.60 6387 2018     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.61 6396 2019     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.62 6397 2019     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.63 6400 2019     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.64 6415 2019     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.65 6416 2019     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.66 6427 2019     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.67 6444 2020     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.68 6450 2020     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.69 6451 2020     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.70 6466 2020     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.71 6478 2020     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.72 6489 2020     PR     PR     PR     PR     PR     PR     PR
## azox_single_pr.73 6494 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr    6001 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.1  6014 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.2  6015 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.3  6020 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.4  6022 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.5  6040 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.6  6041 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.7  6045 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.8  6054 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.9  6055 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.10 6057 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.11 6064 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.12 6069 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.13 6070 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.14 6071 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.15 6074 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.16 6090 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.17 6094 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.18 6100 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.19 6111 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.20 6112 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.21 6124 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.22 6132 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.23 6133 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.24 6145 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.25 6149 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.26 6150 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.27 6162 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.28 6171 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.29 6179 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.30 6181 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.31 6190 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.32 6193 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.33 6201 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.34 6204 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.35 6225 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.36 6229 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.37 6240 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.38 6241 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.39 6250 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.40 6253 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.41 6270 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.42 6273 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.43 6280 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.44 6281 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.45 6284 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.46 6285 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.47 6291 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.48 6297 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.49 6299 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.50 6302 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.51 6318 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.52 6336 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.53 6341 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.54 6350 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.55 6352 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.56 6357 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.57 6360 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.58 6374 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.59 6376 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.60 6387 2018     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.61 6396 2019     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.62 6397 2019     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.63 6400 2019     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.64 6415 2019     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.65 6416 2019     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.66 6427 2019     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.67 6444 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.68 6450 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.69 6451 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.70 6466 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.71 6478 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.72 6489 2020     PR     PR     PR     PR     PR     PR     PR
## pyra_single_pr.73 6494 2020     PR     PR     PR     PR     PR     PR     PR
##                   state8 state9 state10 state11 state12 state13 state14 state15
## azox_single_pr        PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.1      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.2      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.3      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.4      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.5      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.6      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.7      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.8      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.9      PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.10     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.11     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.12     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.13     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.14     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.15     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.16     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.17     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.18     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.19     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.20     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.21     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.22     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.23     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.24     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.25     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.26     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.27     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.28     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.29     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.30     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.31     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.32     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.33     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.34     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.35     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.36     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.37     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.38     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.39     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.40     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.41     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.42     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.43     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.44     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.45     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.46     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.47     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.48     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.49     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.50     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.51     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.52     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.53     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.54     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.55     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.56     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.57     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.58     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.59     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.60     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.61     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.62     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.63     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.64     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.65     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.66     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.67     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.68     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.69     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.70     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.71     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.72     PR     PR      PR      PR      PR      PR      PR      PR
## azox_single_pr.73     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr        PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.1      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.2      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.3      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.4      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.5      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.6      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.7      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.8      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.9      PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.10     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.11     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.12     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.13     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.14     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.15     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.16     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.17     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.18     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.19     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.20     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.21     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.22     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.23     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.24     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.25     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.26     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.27     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.28     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.29     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.30     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.31     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.32     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.33     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.34     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.35     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.36     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.37     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.38     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.39     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.40     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.41     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.42     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.43     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.44     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.45     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.46     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.47     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.48     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.49     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.50     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.51     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.52     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.53     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.54     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.55     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.56     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.57     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.58     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.59     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.60     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.61     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.62     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.63     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.64     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.65     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.66     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.67     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.68     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.69     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.70     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.71     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.72     PR     PR      PR      PR      PR      PR      PR      PR
## pyra_single_pr.73     PR     PR      PR      PR      PR      PR      PR      PR
##                   state16 state17           state18      fungicide
## azox_single_pr         PR      PR                PR   Azoxystrobin
## azox_single_pr.1       PR      PR                PR   Azoxystrobin
## azox_single_pr.2       PR      PR                PR   Azoxystrobin
## azox_single_pr.3       PR      PR                PR   Azoxystrobin
## azox_single_pr.4       PR      PR                PR   Azoxystrobin
## azox_single_pr.5       PR      PR                PR   Azoxystrobin
## azox_single_pr.6       PR      PR                PR   Azoxystrobin
## azox_single_pr.7       PR      PR                PR   Azoxystrobin
## azox_single_pr.8       PR      PR                PR   Azoxystrobin
## azox_single_pr.9       PR      PR                PR   Azoxystrobin
## azox_single_pr.10      PR      PR                PR   Azoxystrobin
## azox_single_pr.11      PR      PR                PR   Azoxystrobin
## azox_single_pr.12      PR      PR                PR   Azoxystrobin
## azox_single_pr.13      PR      PR                PR   Azoxystrobin
## azox_single_pr.14      PR      PR                PR   Azoxystrobin
## azox_single_pr.15      PR      PR                PR   Azoxystrobin
## azox_single_pr.16      PR      PR                PR   Azoxystrobin
## azox_single_pr.17      PR      PR                PR   Azoxystrobin
## azox_single_pr.18      PR      PR                PR   Azoxystrobin
## azox_single_pr.19      PR      PR                PR   Azoxystrobin
## azox_single_pr.20      PR      PR                PR   Azoxystrobin
## azox_single_pr.21      PR      PR                PR   Azoxystrobin
## azox_single_pr.22      PR      PR                PR   Azoxystrobin
## azox_single_pr.23      PR      PR                PR   Azoxystrobin
## azox_single_pr.24      PR      PR                PR   Azoxystrobin
## azox_single_pr.25      PR      PR                PR   Azoxystrobin
## azox_single_pr.26      PR      PR                PR   Azoxystrobin
## azox_single_pr.27      PR      PR                PR   Azoxystrobin
## azox_single_pr.28      PR      PR                PR   Azoxystrobin
## azox_single_pr.29      PR      PR                PR   Azoxystrobin
## azox_single_pr.30      PR      PR                PR   Azoxystrobin
## azox_single_pr.31      PR      PR                PR   Azoxystrobin
## azox_single_pr.32      PR      PR                PR   Azoxystrobin
## azox_single_pr.33      PR      PR                PR   Azoxystrobin
## azox_single_pr.34      PR      PR                PR   Azoxystrobin
## azox_single_pr.35      PR      PR                PR   Azoxystrobin
## azox_single_pr.36      PR      PR                PR   Azoxystrobin
## azox_single_pr.37      PR      PR                PR   Azoxystrobin
## azox_single_pr.38      PR      PR                PR   Azoxystrobin
## azox_single_pr.39      PR      PR                PR   Azoxystrobin
## azox_single_pr.40      PR      PR                PR   Azoxystrobin
## azox_single_pr.41      PR      PR                PR   Azoxystrobin
## azox_single_pr.42      PR      PR                PR   Azoxystrobin
## azox_single_pr.43      PR      PR                PR   Azoxystrobin
## azox_single_pr.44      PR      PR                PR   Azoxystrobin
## azox_single_pr.45      PR      PR                PR   Azoxystrobin
## azox_single_pr.46      PR      PR                PR   Azoxystrobin
## azox_single_pr.47      PR      PR                PR   Azoxystrobin
## azox_single_pr.48      PR      PR                PR   Azoxystrobin
## azox_single_pr.49      PR      PR 0.441767068273094   Azoxystrobin
## azox_single_pr.50      PR      PR                PR   Azoxystrobin
## azox_single_pr.51      PR      PR                PR   Azoxystrobin
## azox_single_pr.52      PR      PR                PR   Azoxystrobin
## azox_single_pr.53      PR      PR                PR   Azoxystrobin
## azox_single_pr.54      PR      PR                PR   Azoxystrobin
## azox_single_pr.55      PR      PR                PR   Azoxystrobin
## azox_single_pr.56      PR      PR                PR   Azoxystrobin
## azox_single_pr.57      PR      PR                PR   Azoxystrobin
## azox_single_pr.58      PR      PR                PR   Azoxystrobin
## azox_single_pr.59      PR      PR                PR   Azoxystrobin
## azox_single_pr.60      PR      PR                PR   Azoxystrobin
## azox_single_pr.61      PR      PR                PR   Azoxystrobin
## azox_single_pr.62      PR      PR                PR   Azoxystrobin
## azox_single_pr.63      PR      PR                PR   Azoxystrobin
## azox_single_pr.64      PR      PR                PR   Azoxystrobin
## azox_single_pr.65      PR      PR                PR   Azoxystrobin
## azox_single_pr.66      PR      PR                PR   Azoxystrobin
## azox_single_pr.67      PR      PR                PR   Azoxystrobin
## azox_single_pr.68      PR      PR                PR   Azoxystrobin
## azox_single_pr.69      PR      PR                PR   Azoxystrobin
## azox_single_pr.70      PR      PR                PR   Azoxystrobin
## azox_single_pr.71      PR      PR                PR   Azoxystrobin
## azox_single_pr.72      PR      PR                PR   Azoxystrobin
## azox_single_pr.73      PR      PR                PR   Azoxystrobin
## pyra_single_pr         PR      PR                PR Pyraclostrobin
## pyra_single_pr.1       PR      PR                PR Pyraclostrobin
## pyra_single_pr.2       PR      PR                PR Pyraclostrobin
## pyra_single_pr.3       PR      PR                PR Pyraclostrobin
## pyra_single_pr.4       PR      PR                PR Pyraclostrobin
## pyra_single_pr.5       PR      PR                PR Pyraclostrobin
## pyra_single_pr.6       PR      PR                PR Pyraclostrobin
## pyra_single_pr.7       PR      PR                PR Pyraclostrobin
## pyra_single_pr.8       PR      PR                PR Pyraclostrobin
## pyra_single_pr.9       PR      PR                PR Pyraclostrobin
## pyra_single_pr.10      PR      PR                PR Pyraclostrobin
## pyra_single_pr.11      PR      PR                PR Pyraclostrobin
## pyra_single_pr.12      PR      PR                PR Pyraclostrobin
## pyra_single_pr.13      PR      PR                PR Pyraclostrobin
## pyra_single_pr.14      PR      PR                PR Pyraclostrobin
## pyra_single_pr.15      PR      PR                PR Pyraclostrobin
## pyra_single_pr.16      PR      PR                PR Pyraclostrobin
## pyra_single_pr.17      PR      PR                PR Pyraclostrobin
## pyra_single_pr.18      PR      PR                PR Pyraclostrobin
## pyra_single_pr.19      PR      PR                PR Pyraclostrobin
## pyra_single_pr.20      PR      PR                PR Pyraclostrobin
## pyra_single_pr.21      PR      PR                PR Pyraclostrobin
## pyra_single_pr.22      PR      PR                PR Pyraclostrobin
## pyra_single_pr.23      PR      PR                PR Pyraclostrobin
## pyra_single_pr.24      PR      PR                PR Pyraclostrobin
## pyra_single_pr.25      PR      PR                PR Pyraclostrobin
## pyra_single_pr.26      PR      PR                PR Pyraclostrobin
## pyra_single_pr.27      PR      PR                PR Pyraclostrobin
## pyra_single_pr.28      PR      PR                PR Pyraclostrobin
## pyra_single_pr.29      PR      PR                PR Pyraclostrobin
## pyra_single_pr.30      PR      PR                PR Pyraclostrobin
## pyra_single_pr.31      PR      PR                PR Pyraclostrobin
## pyra_single_pr.32      PR      PR                PR Pyraclostrobin
## pyra_single_pr.33      PR      PR                PR Pyraclostrobin
## pyra_single_pr.34      PR      PR                PR Pyraclostrobin
## pyra_single_pr.35      PR      PR                PR Pyraclostrobin
## pyra_single_pr.36      PR      PR                PR Pyraclostrobin
## pyra_single_pr.37      PR      PR                PR Pyraclostrobin
## pyra_single_pr.38      PR      PR                PR Pyraclostrobin
## pyra_single_pr.39      PR      PR                PR Pyraclostrobin
## pyra_single_pr.40      PR      PR                PR Pyraclostrobin
## pyra_single_pr.41      PR      PR                PR Pyraclostrobin
## pyra_single_pr.42      PR      PR                PR Pyraclostrobin
## pyra_single_pr.43      PR      PR                PR Pyraclostrobin
## pyra_single_pr.44      PR      PR                PR Pyraclostrobin
## pyra_single_pr.45      PR      PR                PR Pyraclostrobin
## pyra_single_pr.46      PR      PR                PR Pyraclostrobin
## pyra_single_pr.47      PR      PR                PR Pyraclostrobin
## pyra_single_pr.48      PR      PR                PR Pyraclostrobin
## pyra_single_pr.49      PR      PR 0.297858099062918 Pyraclostrobin
## pyra_single_pr.50      PR      PR                PR Pyraclostrobin
## pyra_single_pr.51      PR      PR                PR Pyraclostrobin
## pyra_single_pr.52      PR      PR                PR Pyraclostrobin
## pyra_single_pr.53      PR      PR                PR Pyraclostrobin
## pyra_single_pr.54      PR      PR                PR Pyraclostrobin
## pyra_single_pr.55      PR      PR                PR Pyraclostrobin
## pyra_single_pr.56      PR      PR                PR Pyraclostrobin
## pyra_single_pr.57      PR      PR                PR Pyraclostrobin
## pyra_single_pr.58      PR      PR                PR Pyraclostrobin
## pyra_single_pr.59      PR      PR                PR Pyraclostrobin
## pyra_single_pr.60      PR      PR                PR Pyraclostrobin
## pyra_single_pr.61      PR      PR                PR Pyraclostrobin
## pyra_single_pr.62      PR      PR                PR Pyraclostrobin
## pyra_single_pr.63      PR      PR                PR Pyraclostrobin
## pyra_single_pr.64      PR      PR                PR Pyraclostrobin
## pyra_single_pr.65      PR      PR                PR Pyraclostrobin
## pyra_single_pr.66      PR      PR                PR Pyraclostrobin
## pyra_single_pr.67      PR      PR                PR Pyraclostrobin
## pyra_single_pr.68      PR      PR                PR Pyraclostrobin
## pyra_single_pr.69      PR      PR                PR Pyraclostrobin
## pyra_single_pr.70      PR      PR                PR Pyraclostrobin
## pyra_single_pr.71      PR      PR                PR Pyraclostrobin
## pyra_single_pr.72      PR      PR                PR Pyraclostrobin
## pyra_single_pr.73      PR      PR                PR Pyraclostrobin

Data frame reorganization

results_pr_1<-results_pr %>%  
summarise(mean=as.numeric(mean), 
         lower=as.numeric(`2.5 %`),
         upper= as.numeric(`97.5 %`),
        isolate= as.numeric(isol),
        fungicide=fungicide, 
        year=year, 
        state= state1) 
head(results_pr_1)
##        mean     lower     upper isolate    fungicide year state
## 1 0.7364621 0.6140498 0.8818142    6001 Azoxystrobin 2018    PR
## 2 0.7276119 0.6044125 0.8743544    6014 Azoxystrobin 2018    PR
## 3 0.1756757 0.1294291 0.2336416    6015 Azoxystrobin 2018    PR
## 4 0.7324415 0.6147185 0.8713419    6020 Azoxystrobin 2018    PR
## 5 0.5600000 0.4627765 0.6753745    6022 Azoxystrobin 2018    PR
## 6 0.6474820 0.5360272 0.7801476    6040 Azoxystrobin 2018    PR

Boxplot (all isolates)

box_2=results_pr_1 %>% 
  ggplot(aes(fungicide, mean), color=lower>0.5)+
  ylim(0,1)+
  geom_jitter(size=2, width = 0.2, color="gray")+
  geom_boxplot(width=0.5, outlier.color = NA, color= "black", fill = NA, size =1)+
  scale_color_colorblind()+
   geom_hline(yintercept = 0.5, color="red")+
  #annotate("text", x=2.5,y=0.6, label="DD", size=4, color="red")+
  #facet_wrap("Population")+
  theme_minimal()+
   labs(y = "",
        x="Fungicides")
box_2

#ggsave("img/box_AZOX.png", dpi = 500, height = 3.5, width = 5)
box_1+box_2+plot_annotation(tag_levels = list(c("A", "B")))+plot_layout(ncol = 2)+
  labs(y= "")

ggsave("img/box_all.png",dpi= 500, height = 8, width = 12)

Join data frame

Here we loaded in the same data frame all values for the discriminatory dose for both fungicides.

DD_ALL= full_join(results_rs_1, results_pr_1) 
## Joining, by = c("mean", "lower", "upper", "isolate", "fungicide", "year", "state")
head(DD_ALL)
##        mean      lower     upper      lat     long isolate    fungicide year
## 1 0.3143813 0.24811116 0.3946388 -28.6394 -53.6062       1 Azoxystrobin 2007
## 2 0.2593857 0.20021332 0.3318673 -28.6394 -53.6062       2 Azoxystrobin 2007
## 3 0.2906574 0.22665879 0.3687068 -28.6394 -53.6062       3 Azoxystrobin 2007
## 4 0.0899654 0.05878903 0.1316153 -28.6394 -53.6062       4 Azoxystrobin 2007
## 5 0.3478261 0.27707870 0.4331362 -28.6394 -53.6062       5 Azoxystrobin 2007
## 6 0.5551839 0.45839222 0.6701048 -28.6394 -53.6062       8 Azoxystrobin 2007
##   state
## 1    RS
## 2    RS
## 3    RS
## 4    RS
## 5    RS
## 6    RS

Boxplot DD

DD_ALL %>% 
  ggplot(aes(state, mean, fill= fungicide))+
  ylim(0,1)+
  geom_boxplot(width=0.5, outlier.color = NA, size =1,  alpha=0.3)+
    geom_jitter(size=1, width = 0.2, color="black")+
  scale_fill_manual(values = c("15","gray90"))+
  #scale_color_colorblind()+
   geom_hline(yintercept = 0.5, color="red")+
  #annotate("text", x=2.5,y=0.6, label="DD", size=4, color="red")+
  facet_wrap(~fungicide)+
  theme_minimal()+
   theme(legend.position = "none")+
   labs(y = "Prop of germinated conidia on the DD in relation to control ",
        x="Collection")

ggsave("img/box_all.png", dpi = 500, height = 6, width = 6)